Method and system for gain-independent data normalization in flow cytometry data and system therefor

Abstract

Methods for normalization of gain-independent analyte data (e.g., flow cytometer data).SOLUTION: Methods according to certain embodiments include detecting light from particles in a sample in a flow stream with an optical detection system having an optical detector, generating a data signal in response to the detected light, normalizing the data signal with a detector gain to generate a gain-normalized data signal, and adjusting the gain-normalized data signal with a scaling factor to generate a scaled data signal. Systems and non-transitory computer-readable storage media configured to perform the subject methods are also provided.SELECTED DRAWING: None

Description

[Background technology]

[0001] Characterization of analytes in biological fluids has become an important part of biological research, medical diagnosis, and the assessment of a patient's overall health and wellness. Detecting analytes in biological fluids, such as human blood or blood-derived products, can provide results that can play a role in determining treatment protocols for patients with various disease states.

[0002] Flow cytometry is a technique used to characterize and frequently sort biological materials, such as cells in a blood sample or particles of interest in another type of biological or chemical sample. Flow cytometers typically include a sample reservoir for receiving a fluid sample, such as a blood sample, and a sheath reservoir containing sheath fluid. The flow cytometer transports particles (including cells) in the fluid sample as a stream of cells into a flow cell, while also directing the sheath fluid into the flow cell. To characterize the components of the flow stream, light is illuminated onto the flow stream. Variations in materials in the flow stream, such as the form or presence of fluorescent labels, can cause variations in the observed light, which enable characterization and separation. To characterize the components of the flow stream, light must impinge on the flow stream and be collected. The light source in a flow cytometer can vary and include one or more broad-spectrum lamps, light-emitting diodes, and single-wavelength lasers. The light source is aligned with the flow stream, and the optical response from the illuminated particles is collected and quantified.

[0003] Isolation of biological particles has been achieved by adding sorting or collection capabilities to flow cytometers. Particles in a sorted stream that are detected as having one or more desired properties are individually isolated from the sample stream by mechanical or electrical removal. A common flow sorting technique utilizes droplet sorting, in which a fluid stream containing linearly sorted particles breaks into droplets. Droplets containing particles of interest are charged and deflected into a collection tube by passing through an electric field. Typically, linearly sorted particles in the stream are characterized as they pass an observation point located directly below the nozzle tip. Once a particle is identified as meeting one or more desired criteria, it is possible to predict the time the particle will reach the droplet breakoff point and break away from the stream into droplets. Ideally, a charge is applied to the fluid stream for a short period just before droplets containing the selected particles break away from the stream, and then grounded immediately after the droplets break away. The droplets being sorted retain their charge as they break away from the fluid stream, while all other droplets remain uncharged.

[0004] Flow cytometers scale measured photodetector data in arbitrary units. The position of a given input optical signal on this arbitrary unit scale can be increased or decreased by changing detector gain settings. A flow cytometer's ability to perform consistently from day to day (defined as producing the same output signal for the same input sample) depends on several factors, such as temperature and optical-mechanical alignment, which can change randomly over time. To maintain performance, manufacturers have traditionally established detector gain settings based on daily quality control (QC) procedures. However, users must choose between optimizing detector settings to maximize signal-to-noise ratio (SNR) performance and adjusting detector settings to ensure consistent sample MFI over time—they cannot achieve both simultaneously. Summary of the Invention

[0005] Aspects of the present disclosure include methods for normalizing gain-independent analyte data (e.g., data from a flow cytometer). The method, according to certain embodiments, includes detecting light from particles in a sample in a flow stream using a light detection system having a photodetector, generating a data signal in response to the detected light, normalizing the data signal by a detector gain to generate a gain-normalized data signal, and adjusting the gain-normalized data signal by a scaling factor to generate a scaled data signal. Systems and non-transitory computer-readable storage media configured to perform the subject methods are also provided.

[0006] In some embodiments, a method includes real-time gain-independent scaling of data signals from a particle analyzer, such as flow cytometry data. In some embodiments, a scaling factor adjusts the gain-normalized data signal to a predetermined mean fluorescence intensity. In some cases, the method includes calculating the scaling factor. In some cases, calculating the scaling factor includes determining a linear gain as a function of photodetector voltage, determining a gain corresponding to the predetermined mean fluorescence intensity, and calculating a scaling factor that adjusts the generated gain-normalized data signal to the predetermined mean fluorescence intensity.

[0007] In some embodiments, the linear gain is derived from a lookup table. In some cases, the method includes determining the linear gain as a function of photodetector voltage by illuminating a photodetector with a light source of multiple different intensities, detecting light from the light sources of multiple different intensities at multiple different photodetector voltages, and determining a detector gain setting for the photodetector sufficient to produce an average fluorescence intensity that increases linearly with detector gain. In some cases, the light source is a light-emitting diode. In some cases, the light source is a laser, such as a continuous wave laser. In some cases, the predetermined average fluorescence intensity is determined by illuminating a reference particle with the light source and detecting fluorescence from the reference particle. In certain cases, the reference particle is a multispectral fluorescent bead. In some embodiments, the detector gain used to normalize the data signal is the gain of the photodetector at the predetermined average fluorescence intensity.

[0008] In some embodiments, the method includes adjusting the gain-normalized data signal with a calibration factor. In some cases, the calibration factor adjusts the gain-normalized data signal in response to changes in particle velocity in the flow stream. In some cases, the calibration factor adjusts the gain-normalized data signal in response to changes in laser intensity of the light source. In some cases, the method includes spectrally separating the gain-normalized data signal and adjusting the spectrally separated data signal with a scaling factor to generate a scaled separated data signal.

[0009] In some embodiments, the light is detected in multiple photodetector channels. In some cases, the method includes illuminating the sample with a light source. In some cases, the light source includes a laser, such as multiple lasers.

[0010] Aspects of the present disclosure also include systems for implementing the subject methods, e.g., for analyzing analyte data by generating a gain-normalized data signal. Systems according to certain embodiments include a light source configured to illuminate a sample having particles in a flowstream, a light detection system having a photodetector for detecting light from the illuminated particles, and a processor, the processor comprising a memory operatively coupled to the processor and having instructions stored in the memory that, when executed by the processor, cause the processor to generate a data signal in response to the detected light, normalize the data signal by a detector gain to generate a gain-normalized data signal, and adjust the gain-normalized data signal by a scaling factor to generate a scaled data signal. In some cases, the system is configured to detect light in multiple photodetector channels by the light detection system. In some cases, the scaling factor adjusts the gain-normalized data signal to a predetermined mean fluorescence intensity.

[0011] In some embodiments, the memory includes instructions for calculating a scaling factor. In some cases, the memory includes instructions for calculating a scaling factor by determining a linear gain as a function of photodetector voltage, determining a gain corresponding to a predetermined mean fluorescence intensity, and calculating a scaling factor that scales the generated gain-normalized data signal to the predetermined mean fluorescence intensity. In some cases, the linear gain is derived from a lookup table. In some embodiments, the memory includes instructions for determining the linear gain as a function of photodetector voltage by illuminating the photodetector with a light source of multiple different intensities, detecting light from the light sources of multiple different intensities at a plurality of different photodetector voltages, and determining a detector gain setting for the photodetector sufficient to produce a mean fluorescence intensity that increases linearly with detector gain. In some cases, the light source includes a light emitting diode.

[0012] In some cases, the memory includes instructions for determining a predetermined average fluorescence intensity by illuminating the reference particles with a light source and detecting fluorescence from the reference particles. In some cases, the reference particles are multispectral beads. In some cases, the memory includes instructions for normalizing the data signal using a detector gain. In some cases, the detector gain used to normalize the data signal is the gain of the photodetector at the predetermined average fluorescence intensity. In some cases, the memory includes instructions for adjusting the gain-normalized data signal with a calibration factor. In some cases, the memory includes instructions for adjusting the gain-normalized data signal with a calibration factor that adjusts the gain-normalized data signal in response to changes in particle velocity in the flow stream. In some cases, the memory includes instructions for adjusting the gain-normalized data signal with a calibration factor that adjusts the gain-normalized data signal in response to changes in laser intensity of the light source. In some cases, the memory includes instructions for spectrally separating the gain-normalized data signal and adjusting the spectrally separated data signal with a scaling factor to generate a scaled separated data signal.

[0013] In some embodiments, the system includes a light source having one or more lasers, such as multiple lasers. In some cases, the light detection system includes multiple photodetectors. In some cases, the photodetector includes one or more photomultiplier tubes. In some cases, the light detection system includes a photodetector array. In certain cases, one or more of the photodetectors in the array are photodiodes. In certain cases, one or more of the photodetectors in the array are charge-coupled devices.

[0014] Aspects of the present disclosure also include non-transitory computer-readable storage media for carrying out, for example, one or more computer-implemented methods described herein. In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for detecting light from particles in a sample in a flow stream using a light detection system having a light detector, an algorithm for generating a data signal in response to the detected light, an algorithm for normalizing the data signal by a detector gain to generate a gain-normalized data signal, and an algorithm for adjusting the gain-normalized data signal by a scaling factor to generate a scaled data signal.

[0015] In some cases, the non-transitory computer-readable storage medium includes an algorithm for applying a scaling factor that adjusts the gain-normalized data signal to a predetermined mean fluorescence intensity. In some cases, the non-transitory computer-readable storage medium includes an algorithm for calculating the scaling factor. In some cases, the non-transitory computer-readable storage medium includes an algorithm for determining a linear gain as a function of photodetector voltage, an algorithm for determining a gain that corresponds to a predetermined mean fluorescence intensity, and an algorithm for calculating a scaling factor that adjusts the generated gain-normalized data signal to the predetermined mean fluorescence intensity.

[0016] In some cases, the non-transitory computer-readable storage medium includes an algorithm for deriving a linear gain from a lookup table. In some cases, the non-transitory computer-readable storage medium includes an algorithm for illuminating a photodetector with a light source of multiple different intensities, an algorithm for detecting light from the light sources of multiple different intensities at multiple different photodetector voltages, and an algorithm for determining a detector gain setting for the photodetector sufficient to produce an average fluorescence intensity that increases linearly with detector gain. In some cases, the non-transitory computer-readable storage medium includes an algorithm for determining a predetermined average fluorescence intensity. In some cases, the non-transitory computer-readable storage medium includes an algorithm for illuminating a reference particle (e.g., a multispectral bead) with a light source and an algorithm for detecting fluorescence from the reference particle. In some cases, the non-transitory computer-readable storage medium includes an algorithm for normalizing a data signal using a detector gain, which is the gain of the photodetector at a predetermined average fluorescence intensity. In some cases, the non-transitory computer-readable storage medium includes an algorithm for adjusting the gain-normalized data signal with a calibration factor. In some cases, the non-transitory computer-readable storage medium includes an algorithm for adjusting the gain-normalized data signal with a calibration factor that adjusts the gain-normalized data signal in response to changes in particle velocity in the flow stream. In some cases, the non-transitory computer-readable storage medium includes an algorithm for adjusting the gain-normalized data signal with a calibration factor that adjusts the gain-normalized data signal in response to changes in laser intensity of the light source. In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for spectrally separating the gain-normalized data signal and an algorithm for adjusting the spectrally separated data signal with a scaling factor to generate a scaled separated data signal.

[0017] The present disclosure can be best understood from the following detailed description when read in conjunction with the accompanying drawings, which include the following figures: [Brief explanation of the drawings]

[0018] [Figure 1A] 1 illustrates a flowchart for normalization of a gain-independent analyte data signal, according to certain embodiments. [Figure 1B] 1 illustrates a step-by-step diagram of generating scaled gain normalized data in accordance with certain embodiments. [Figure 1C] 10 illustrates a comparison of scatter plot analyses of sample data generated using gain-dependent and gain-independent (gain-normalized) data signals, according to certain embodiments. [Figure 1D] FIG. 1 shows a diagram of scaling a data signal from a flow cytometer, according to certain embodiments. [Figure 1E] 1 illustrates the use of scaled and unscaled data signals in accordance with certain embodiments. [Figure 1F] 1 illustrates a comparison of data analysis with and without gain scaling, according to certain embodiments. [Figure 2] 1 illustrates a flow cytometry system according to certain embodiments. [Figure 3-1] 1 illustrates an image-enabled particle sorter in accordance with certain embodiments. [Figure 3-2] 1 illustrates an image-enabled particle sorter in accordance with certain embodiments. [Figure 4] FIG. 1 illustrates a functional block diagram of a particle analysis system in accordance with certain embodiments. [Figure 5] FIG. 1 illustrates a functional block diagram of an example control system in accordance with certain embodiments. [Figure 6A] 1 illustrates a schematic diagram of a particle sorter system in accordance with certain embodiments. [Figure 6B] 1 illustrates a schematic diagram of a particle sorter system in accordance with certain embodiments. [Figure 7] 1 illustrates aspects of a computer control system according to certain embodiments. [Figure 8]1 is an example of an APD detector gain calibration. Once an instrument baseline is performed, each detector undergoes calibration using the mcLED to determine the relationship between gain control and observed detector gain. [Figure 9] Examples of individual components of TTV rescaling, including default scaling, gain-independent scaling, and ABD scaling. Panel A is default flow cytometry data scaling vs. gain (dB), Panel B is gain-independent flow cytometry scaling vs. gain (dB), and Panel C is ABD flow cytometry data scaling vs. gain (dB). [Figure 10] An example demonstrating the need for dynamic scaling with the introduction of ABD scaling. Panel A is the default flow cytometry data scaling vs. gain (dB), and panel B is the ABD flow cytometry scaling vs. gain (dB). Black populations represent dim / negative beads, and red populations represent bright / positive beads. [Figure 11] This is an example of converting raw data biexponential transformation variables to create consistent data across scaling modes. The left column shows the default scaling mode, and the right column shows the DSI scaling mode, where the biexponential transformation is adjusted to account for the DSI coefficients. The data shows 8-peak rainbow beads acquired at different detector gain settings, with each row having a different gain setting annotated in the axis labels. The axis labels also show the T and r values ​​used to generate the biexponential transformation, with values ​​rounded to one decimal place. DETAILED DESCRIPTION OF THE INVENTION

[0019] Aspects of the present disclosure include methods for normalizing gain-independent analyte data (e.g., flow cytometer data). The method, according to certain embodiments, includes detecting light from particles in a sample in a flow stream using a light detection system having a photodetector, generating a data signal in response to the detected light, normalizing the data signal by a detector gain to generate a gain-normalized data signal, and adjusting the gain-normalized data signal by a scaling factor to generate a scaled data signal. Systems and non-transitory computer-readable storage media configured to perform the subject methods are also provided.

[0020] Before describing the present disclosure in more detail, it is to be understood that this disclosure is not limited to particular embodiments described, as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present disclosure will be limited only by the appended claims.

[0021] Where a range of values ​​is presented, unless the context clearly dictates otherwise, it is understood that each intervening value, to the tenth of the unit of the lower limit, between the upper and lower limits of that range, and any other stated or intervening value within that stated range, is encompassed within the disclosure. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges, and are also encompassed within the disclosure, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the disclosure.

[0022]

[0023] Certain ranges are presented herein with numerical values ​​preceded by the term "about." The term "about" is used herein to provide literal support for the exact number it precedes, as well as a number that is near or approximately the number it precedes. In determining whether a number is near or approximately a specifically recited number, the unrecited near or approximately number may be a number that, in the context in which the number is presented, represents a substantial equivalent to the specifically recited number.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. Although any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of this disclosure, representative exemplary methods and materials are now described.

[0024] All publications and patents cited herein are incorporated by reference to disclose and describe the methods and / or materials for which the publications are cited, as if each individual publication or patent was specifically and individually indicated to be incorporated by reference. The citation of any publication is for its disclosure prior to the filing date and should not be construed as an admission that the present disclosure is not entitled to antedate such publication by virtue of prior disclosure. Further, the dates of publication provided may be different from the actual publication dates, which may need to be independently confirmed.

[0025] It should be noted that, as used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. It should be further noted that the claims may be drafted to exclude optional elements. Accordingly, this statement is intended to serve as a predicate for use of exclusive terminology, such as "solely," "only," and the like, in connection with the recitation of claim elements or the use of a "negative" limitation.

[0026] As will be apparent to those skilled in the art upon reading this disclosure, each of the individual embodiments described and illustrated herein has discrete components and features that may be readily separated from or combined with the features of any of the other several embodiments without departing from the scope or spirit of the disclosure. Any recited method can be carried out in the order of events recited or in any other order that is logically possible.

[0027] Although the systems and methods have been or will be described for grammatical fluidity with functional descriptions, it is to be clearly understood that the claims should not be construed as necessarily limited by "means" or "step" limitation constructions unless expressly formulated under 35 U.S.C. § 112, but rather should be given the full scope of meaning and equivalents of the definitions provided by the claims under the doctrine of equivalents, and that if a claim is expressly formulated under 35 U.S.C. § 112, the full legal equivalents under 35 U.S.C. § 112 should be given.

[0028] Methods for normalizing gain-independent analyte data Aspects of the present disclosure include methods for normalizing gain-independent analyte data (e.g., data from a flow cytometer). In some embodiments, the subject methods provide real-time gain-independent scaling of data signals from a particle analyzer. In some cases, the subject methods provide a sample mean fluorescence intensity that is maintained regardless of data settings, so that the numerical scale used to represent mean fluorescence intensity (MFI) corresponds to the true intensity of the input signal. In certain embodiments, the subject methods provide for correcting for inter-instrument variations by accounting for inter-instrument tolerances, such as particle velocity and laser intensity. In some cases, this provides for easy and efficient inter-instrument cross-analysis using the same instrument acquisition and data analysis template with faster and more accurate downstream intra-platform analysis and longitudinal analysis. Furthermore, when data signals are processed and scaled according to methods according to certain embodiments, detector gain adjustments (such as those performed during quality control evaluation) maintain the resolution performance of the cytometer, for example, by adjusting gain settings to maximize the signal-to-noise ratio (SNR). In some cases, the method includes providing calibration-free consistency within a single device over time, for example, for 1 day or more, for example, 3 days or more, for example, 7 days or more, for example, 2 weeks or more, for example, 4 weeks or more, for example, 3 months or more, for example, 6 months or more, for example, 9 months or more, and maintaining calibration-free consistency within the device for 1 year or more.

[0029] In certain embodiments, the subject methods provide optimized photodetector system performance, such as an increase in the signal-to-noise ratio of the photodetection system. For example, the signal-to-noise ratio of the photodetection system can be increased by 5% or more, for example, 10% or more, for example, 25% or more, for example, 50% or more, for example, 75% or more, for example, 90% or more, and for example, 99% or more. In certain cases, the subject methods increase the signal-to-noise ratio by 2 times or more, for example, 3 times or more, for example, 4 times or more, for example, 5 times or more, and for example, 10 times or more. In some embodiments, the subject methods increase the consistency of the average fluorescence intensity output from the photodetector of the photodetection system by 5% or more, for example, 10% or more, for example, 25% or more, for example, 50% or more, for example, 75% or more, for example, 90% or more, and for example, 99% or more. In certain cases, the subject methods increase the consistency of the average fluorescence intensity output from the photodetector of the photodetection system by 2 times or more, for example, 3 times or more, for example, 4 times or more, for example, 5 times or more, and for example, 10 times or more.

[0030] The term "analyte data" is used herein in its conventional sense to refer to data obtained by evaluating a particular analyte for a particular property. In some cases, the analyte data is flow cytometer data. "Flow cytometer data" refers to information about the properties of sample particles collected by any number of detectors in a particle analyzer. As discussed herein, a "particle analyzer" is an analytical tool (e.g., a flow cytometer) that enables the characterization of particles based on specific (e.g., optical) parameters. "Particle" refers to a discrete component of a biological sample, such as a molecule, an analyte-bound bead, or an individual cell. While the present disclosure is primarily described with respect to flow cytometer data, the applicability of the present disclosure is not limited to flow cytometer data. In certain cases, the present disclosure may be applicable to other types of data, such as nucleic acid data.

[0031] The flow cytometer data may be received from any suitable source. In some embodiments, the flow cytometer data is received from a memory of a storage device. In such embodiments, the flow cytometer data may be pre-generated and stored in the memory of a storage device for subsequent retrieval and analysis. In other embodiments, the flow cytometer data is received in real time. In other words, the flow cytometer data generated during operation of the flow cytometer may then (e.g., immediately) be loaded into a data space (e.g., a two-dimensional plot). In embodiments, the flow cytometer data is received from a forward scatter detector. The forward scatter detector may, in some cases, provide information regarding the overall size of the particle. In embodiments, the flow cytometer data is received from a side scatter detector. The side scatter detector may, in some cases, be configured to detect refracted and reflected light from the surface and internal structure of the particle, which tends to increase as the particle structure becomes more complex.

[0032] In certain embodiments, particles are detected and uniquely identified by exposing them to excitation light and measuring the fluorescence of each particle in one or more detection channels, as desired. The fluorescence emitted in the detection channels used to identify particles and their associated binding complexes can be measured after excitation by a single light source or separately after excitation by separate light sources. When separate excitation light sources are used to excite particle labels, the labels can be selected so that all labels are excitable by each of the excitation light sources used. In embodiments, flow cytometer data is received from a fluorescence detector. The fluorescence detector can, in some cases, be configured to detect fluorescent emissions from fluorescent molecules, such as labeled specific binding members (e.g., labeled antibodies that specifically bind to a marker of interest) associated with particles in the flow cell. In certain embodiments, the method includes detecting fluorescence from the sample using one or more fluorescence detectors, e.g., two or more, e.g., three or more, e.g., four or more, e.g., five or more, e.g., six or more, e.g., seven or more, e.g., eight or more, e.g., nine or more, e.g., ten or more, e.g., fifteen or more, and e.g., twenty-five or more fluorescence detectors. In embodiments, each of the fluorescence detectors is configured to generate a fluorescence data signal. Fluorescence from the sample can be independently detected by each fluorescence detector over one or more wavelength ranges from 200 nm to 1200 nm. In some cases, the method includes detecting fluorescence from the sample over wavelength ranges, e.g., from 200 nm to 1200 nm, e.g., from 300 nm to 1100 nm, e.g., from 400 nm to 1000 nm, e.g., from 500 nm to 900 nm, and e.g., from 600 nm to 800 nm. In other cases, the method includes detecting fluorescence using each fluorescence detector at one or more specific wavelengths. For example, fluorescence may be detected at one or more of 450 nm, 518 nm, 519 nm, 561 nm, 578 nm, 605 nm, 607 nm, 625 nm, 650 nm, 660 nm, 667 nm, 670 nm, 668 nm, 695 nm, 710 nm, 723 nm, 780 nm, 785 nm, 647 nm, 617 nm, and any combination thereof, depending on the number of different fluorescence detectors in the subject optical detection system.In certain embodiments, the method includes detecting wavelengths of light corresponding to the fluorescence peak wavelengths of particular fluorophores present in the sample. In embodiments, the flow cytometer data is received from one or more photodetectors (e.g., one or more detection channels), such as two or more, such as three or more, such as four or more, such as five or more, such as six or more, and such as eight or more photodetectors (e.g., eight or more detection channels).

[0033] In practicing the subject methods, a sample having particles (e.g., beads of a calibration composition, as described in more detail below) in a flow stream is irradiated with light from a light source. In some embodiments, the light source is a broadband light source that emits light having a wide range of wavelengths, e.g., 50 nm or greater, e.g., 100 nm or greater, e.g., 150 nm or greater, e.g., 200 nm or greater, e.g., 250 nm or greater, e.g., 300 nm or greater, e.g., 350 nm or greater, e.g., 400 nm or greater, and e.g., 500 nm or greater. For example, one suitable broadband light source emits light having a wavelength between 200 nm and 1500 nm. Another example of a suitable broadband light source includes a light source that emits light having a wavelength between 400 nm and 1000 nm. Where the method includes irradiating a broadband light source, the broadband light source protocol of interest may include, but is not limited to, a halogen lamp, a deuterium arc lamp, a xenon arc lamp, a stabilized fiber-coupled broadband light source, a broadband LED with a continuous spectrum, a superluminescent light emitting diode, a semiconductor light emitting diode, a wide spectrum LED white light source, a multi-LED integrated white light source, or any combination thereof, among other broadband light sources.

[0034] In other embodiments, the method includes irradiating a narrowband light source that emits a specific wavelength or a narrow range of wavelengths, for example, a light source that emits light in a narrow range, such as a range of 50 nm or less, for example, 40 nm or less, for example, 30 nm or less, for example, 25 nm or less, for example, 20 nm or less, for example, 15 nm or less, for example, 10 nm or less, for example, 5 nm or less, for example, 2 nm or less, and a light source that emits a specific wavelength of light (i.e., monochromatic light). When the method includes irradiating a narrowband light source, the narrowband light source protocol of interest may include, but is not limited to, a narrow wavelength LED, a laser diode, or a broadband light source coupled to one or more optical bandpass filters, a diffraction grating, a monochromator, or any combination thereof.

[0035] In certain embodiments, the method includes irradiating the flowstream with one or more lasers. The type and number of lasers vary depending on the sample and the desired light to be collected and can be pulsed or continuous wave lasers. For example, the laser can be a gas laser such as a helium-neon laser, an argon laser, a krypton laser, a xenon laser, a nitrogen laser, a CO2 laser, a CO2 laser, an argon-fluorine (ArF) excimer laser, a krypton-fluorine (KrF) excimer laser, a xenon-chlorine (XeCl) excimer laser, a xenon-fluorine (XeF) excimer laser, or a combination thereof; a dye laser such as a stilbene, coumarin, or rhodamine laser; a helium-cadmium (HeCd) laser, a helium-mercury (HeHg) laser, a helium-selenium (HeSe) laser, a helium-silver (HeAg) laser, a strontium laser, a neon-copper ( metal vapor lasers such as Nd:NeCu (NeCu) lasers, copper lasers or gold lasers, and combinations thereof; solid state lasers such as ruby ​​lasers, Nd:YAG lasers, NdCrYAG lasers, Er:YAG lasers, Nd:YLF lasers, Nd:YVO4 lasers, Nd:YCa4O(BO3)3 lasers, Nd:YCOB lasers, titanium sapphire lasers, thulium YAG lasers, ytterbium YAG lasers, ytterbium2O3 lasers or cerium doped lasers, and combinations thereof; semiconductor diode lasers, optically pumped semiconductor lasers (OPSLs), or frequency doubled or frequency tripled implementations of any of the above lasers.

[0036] The sample in the flowstream can be illuminated with one or more of the above-mentioned light sources, for example, two or more light sources, for example, three or more light sources, for example, four or more light sources, for example, five or more light sources, and for example, ten or more light sources. The light sources may include any combination of light source types. For example, in some embodiments, the method includes illuminating the sample in the flowstream with an array of lasers, such as an array having one or more gas lasers, one or more dye lasers, and one or more solid-state lasers.

[0037] The sample may be irradiated with wavelengths ranging from 200 nm to 1500 nm, e.g., 250 nm to 1250 nm, e.g., 300 nm to 1000 nm, e.g., 350 nm to 900 nm, and e.g., 400 nm to 800 nm. For example, if the light source is a broadband light source, the sample may be irradiated with wavelengths ranging from 200 nm to 900 nm. In other cases, if the light source includes multiple narrowband light sources, the sample may be irradiated with specific wavelengths ranging from 200 nm to 900 nm. For example, the light source may be multiple narrowband LEDs (1 nm to 25 nm) that each independently emit light having a wavelength range of 200 nm to 900 nm. In other embodiments, the narrowband light source includes one or more lasers (e.g., a laser array), and the sample is irradiated with specific wavelengths ranging from 200 nm to 700 nm using a laser array including the gas lasers, excimer lasers, dye lasers, metal vapor lasers, and solid-state lasers described above.

[0038] When two or more light sources are used, the sample can be illuminated by the light sources simultaneously, sequentially, or a combination thereof. For example, the sample can be illuminated by each of the light sources simultaneously. In other embodiments, the flow stream is illuminated sequentially by each of the light sources. When two or more light sources are used to sequentially illuminate the sample, the time for which each light source illuminates the sample can independently be 0.001 microseconds or more, e.g., 0.01 microseconds or more, e.g., 0.1 microseconds or more, e.g., 1 microsecond or more, e.g., 5 microseconds or more, e.g., 10 microseconds or more, e.g., 30 microseconds or more, and e.g., 60 microseconds or more. For example, the method can include irradiating the sample with a light source (e.g., a laser) for a duration ranging from 0.001 microseconds to 100 microseconds, e.g., 0.01 microseconds to 75 microseconds, e.g., 0.1 microseconds to 50 microseconds, e.g., 1 microsecond to 25 microseconds, and e.g., 5 microseconds to 10 microseconds. In embodiments in which the sample is illuminated sequentially with two or more light sources, the duration for which the sample is illuminated by each light source may be the same or different.

[0039] The time period between illumination by each light source can also vary, as desired, independently separated by a delay of 0.001 microseconds or more, e.g., 0.01 microseconds or more, e.g., 0.1 microseconds or more, e.g., 1 microsecond or more, e.g., 5 microseconds or more, e.g., 10 microseconds or more, e.g., 15 microseconds or more, e.g., 30 microseconds or more, and e.g., 60 microseconds or more. For example, the time period between illumination by each light source can range from 0.001 microseconds to 60 microseconds, e.g., 0.01 microseconds to 50 microseconds, e.g., 0.1 microseconds to 35 microseconds, e.g., 1 microsecond to 25 microseconds, and e.g., 5 microseconds to 10 microseconds. In certain embodiments, the time period between illumination by each light source is 10 microseconds. In embodiments in which the sample is illuminated sequentially by more than two (i.e., three or more) light sources, the delay between illumination by each light source can be the same or different.

[0040] The sample can be illuminated continuously or at discrete intervals. In some cases, the method includes continuously illuminating the sample with a light source. In other cases, the sample is illuminated by the light source at discrete intervals, such as every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds, and every 1000 milliseconds, or some other interval.

[0041] Depending on the light source, the sample may be illuminated from various distances, such as 0.01 mm or more, for example 0.05 mm or more, for example 0.1 mm or more, for example 0.5 mm or more, for example 1 mm or more, for example 2.5 mm or more, for example 5 mm or more, for example 10 mm or more, for example 15 mm or more, for example 25 mm or more, and for example 50 mm or more. Also, the angle or illumination may vary in the range of 10° to 90°, for example 15° to 85°, for example 20° to 80°, for example 25° to 75°, and for example 30° to 60°, for example an angle of 90°.

[0042] In performing the subject methods, light from the illuminated sample is measured, such as by collecting light from the sample over a range of wavelengths (e.g., 200 nm to 1000 nm). In embodiments, the methods may include one or more of measuring light absorption by the sample (e.g., brightfield light data), measuring light scattering (e.g., forward scattered light data or side scattered light data), and measuring light emission by the sample (e.g., fluorescence data).

[0043] As described above, an optical beam generator component having a laser and an acousto-optical device for frequency-shifting the laser light can be used. In these embodiments, the method includes illuminating the acousto-optical device with a laser. Depending on the desired wavelength of light generated in the output laser beam (e.g., for use in illuminating a sample in a flow stream), the laser may have a specific wavelength that varies from 200 nm to 1500 nm, e.g., 250 nm to 1250 nm, e.g., 300 nm to 1000 nm, e.g., 350 nm to 900 nm, and e.g., 400 nm to 800 nm. The acousto-optical device can be illuminated with one or more lasers, e.g., two or more lasers, e.g., three or more lasers, e.g., four or more lasers, e.g., five or more lasers, and e.g., ten or more lasers. The lasers can include any combination of lasers. For example, in some embodiments, the method includes illuminating the acousto-optical device with an array of lasers, such as an array having one or more gas lasers, one or more dye lasers, and one or more solid-state lasers.

[0044] When two or more lasers are used, the acousto-optical device can be illuminated by the lasers simultaneously, sequentially, or a combination thereof. For example, the acousto-optical device can be illuminated by each of the lasers simultaneously. In other embodiments, the acousto-optical device is illuminated sequentially by each of the lasers. When two or more lasers are used to sequentially illuminate the acousto-optical device, the time for which each laser illuminates the acousto-optical device can independently be 0.001 microseconds or more, e.g., 0.01 microseconds or more, e.g., 0.1 microseconds or more, e.g., 1 microsecond or more, e.g., 5 microseconds or more, e.g., 10 microseconds or more, e.g., 30 microseconds or more, and e.g., 60 microseconds or more. For example, the method can include illuminating the acousto-optical device with a laser for a duration ranging from 0.001 microseconds to 100 microseconds, e.g., 0.01 microseconds to 75 microseconds, e.g., 0.1 microseconds to 50 microseconds, e.g., 1 microsecond to 25 microseconds, and e.g., 5 microseconds to 10 microseconds. In embodiments in which the acousto-optic device is illuminated sequentially with two or more lasers, the duration for which the acousto-optic device is illuminated by each laser may be the same or different.

[0045] The time period between irradiation by each laser can also vary, as desired, independently separated by a delay of 0.001 microseconds or more, e.g., 0.01 microseconds or more, e.g., 0.1 microseconds or more, e.g., 1 microsecond or more, e.g., 5 microseconds or more, e.g., 10 microseconds or more, e.g., 15 microseconds or more, e.g., 30 microseconds or more, and e.g., 60 microseconds or more. For example, the time period between irradiation by each laser can range from 0.001 microseconds to 60 microseconds, e.g., 0.01 microseconds to 50 microseconds, e.g., 0.1 microseconds to 35 microseconds, e.g., 1 microsecond to 25 microseconds, and e.g., 5 microseconds to 10 microseconds. In certain embodiments, the time period between irradiation by each laser is 10 microseconds. In embodiments in which the acousto-optic device is sequentially illuminated by more than two (i.e., three or more) lasers, the delay between irradiation by each laser can be the same or different.

[0046] The acousto-optic device can be illuminated continuously or at discrete intervals. In some cases, the method includes continuously illuminating the acousto-optic device with a laser. In other cases, the acousto-optic device is illuminated with a laser at discrete intervals, such as every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds, and every 1000 milliseconds, or some other interval.

[0047] Depending on the laser, the acousto-optic device may be illuminated from a variety of distances, such as 0.01 mm or more, for example 0.05 mm or more, for example 0.1 mm or more, for example 0.5 mm or more, for example 1 mm or more, for example 2.5 mm or more, for example 5 mm or more, for example 10 mm or more, for example 15 mm or more, for example 25 mm or more, and for example 50 mm or more, and the angle or illumination may vary in the range of 10° to 90°, for example 15° to 85°, for example 20° to 80°, for example 25° to 75°, and for example 30° to 60°, for example an angle of 90°.

[0048] In an embodiment, a method includes applying high frequency drive signals to an acousto-optic device to generate angularly deflected laser beams. Two or more high frequency drive signals, for example, three or more high frequency drive signals, for example, four or more high frequency drive signals, for example, five or more high frequency drive signals, for example, six or more high frequency drive signals, for example, seven or more high frequency drive signals, for example, eight or more high frequency drive signals, for example, nine or more high frequency drive signals, for example, ten or more high frequency drive signals, for example, fifteen or more high frequency drive signals, for example, twenty-five or more high frequency drive signals, for example, fifty or more high frequency drive signals, and for example, one hundred or more high frequency drive signals may be applied to the acousto-optic device to generate an output laser beam having a desired number of angularly deflected laser beams.

[0049] Each angularly deflected laser beam generated by the high frequency drive signal has an intensity based on the amplitude of the applied high frequency drive signal. In some embodiments, a method includes applying a high frequency drive signal having an amplitude sufficient to generate an angularly deflected laser beam having a desired intensity. In some cases, each applied high frequency drive signal independently has an amplitude of about 0.001 V to about 500 V, e.g., about 0.005 V to about 400 V, e.g., about 0.01 V to about 300 V, e.g., about 0.05 V to about 200 V, e.g., about 0.1 V to about 100 V, e.g., about 0.5 V to about 75 V, e.g., about 1 V to about 50 V, e.g., about 2 V to about 40 V, e.g., 3 V to about 30 V, and e.g., about 5 V to about 25 V. In some embodiments, each applied high frequency drive signal has a frequency of about 0.001 MHz to about 500 MHz, for example, about 0.005 MHz to about 400 MHz, for example, about 0.01 MHz to about 300 MHz, for example, about 0.05 MHz to about 200 MHz, for example, about 0.1 MHz to about 100 MHz, for example, about 0.5 MHz to about 90 MHz, for example, about 1 MHz to about 75 MHz, for example, about 2 MHz to about 70 MHz, for example, about 3 MHz to about 65 MHz, for example, about 4 MHz to about 60 MHz, and for example, about 5 MHz to about 50 MHz.

[0050] In some embodiments, the sample in the flow stream is illuminated with an output laser beam from the acousto-optic device, which includes angularly deflected laser beams, each having an intensity based on the amplitude of the applied high-frequency drive signal. For example, the output laser beam used to illuminate particles in the flow stream can include two or more angularly deflected laser beams, such as three or more, such as four or more, such as five or more, such as six or more, such as seven or more, such as eight or more, such as nine or more, such as ten or more, and such as twenty-five or more angularly deflected laser beams. In embodiments, each of the angularly deflected laser beams has a different frequency that is shifted from the frequency of the input laser beam by a predetermined high frequency.

[0051] Each angularly deflected laser beam is also spatially shifted relative to one another. Depending on the applied high frequency drive signal and the desired illumination profile of the output laser beam, the angularly deflected laser beams can be separated by 0.001 μm or more, for example, 0.005 μm or more, for example, 0.01 μm or more, for example, 0.05 μm or more, for example, 0.1 μm or more, for example, 0.5 μm or more, for example, 1 μm or more, for example, 5 μm or more, for example, 10 μm or more, for example, 100 μm or more, for example, 500 μm or more, for example, 1,000 μm or more, and for example, 5,000 μm or more. In some embodiments, the angularly deflected laser beams overlap with adjacent angularly deflected laser beams along the horizontal axis of the output laser beam. The overlap between adjacent angularly deflected laser beams (e.g., beam spot overlap) can be an overlap of 0.001 μm or more, such as an overlap of 0.005 μm or more, for example an overlap of 0.01 μm or more, for example an overlap of 0.05 μm or more, for example an overlap of 0.1 μm or more, for example an overlap of 0.5 μm or more, for example an overlap of 1 μm or more, for example an overlap of 5 μm or more, for example an overlap of 10 μm or more, and for example an overlap of 100 μm or more.

[0052] In certain instances, see Diebold, et al. Nature Photonics Vol. 7(10); 806-810 (2013) and U.S. Patent Nos. 9,423,353, 9,784,661, 9,983,132, 10,006,852, 10,078,045, 10,036,699, 10,222,316, 10,288,546, 10,324,019, 10,408,758, 10,451,538, 10,620,111, and and U.S. Patent Application Publication Nos. 2017 / 0133857, 2017 / 0328826, 2017 / 0350803, 2018 / 0275042, 2019 / 0376895, and 2019 / 0376894, the disclosures of which are incorporated herein by reference, whereby multiple frequency-shifted light beams are irradiated onto a sample in the flow stream to generate images of cell nuclei in the flow stream.

[0053] As described above, in embodiments, light from the illuminated sample is conveyed to a light detection system, as described in more detail below, and measured by multiple light detectors. In some embodiments, the method includes measuring collected light over a wavelength range (e.g., 200 nm to 1000 nm). For example, the method may include collecting a spectrum of light over one or more wavelength ranges from 200 nm to 1000 nm. In still other embodiments, the method includes measuring collected light at one or more specific wavelengths. For example, collected light may be measured at one or more of 450 nm, 518 nm, 519 nm, 561 nm, 578 nm, 605 nm, 607 nm, 625 nm, 650 nm, 660 nm, 667 nm, 670 nm, 668 nm, 695 nm, 710 nm, 723 nm, 780 nm, 785 nm, 647 nm, 617 nm, and any combination thereof. In certain embodiments, the method comprises measuring a wavelength of light corresponding to the fluorescence peak wavelength of the fluorophore, hi some embodiments, the method comprises measuring light collected across the fluorescence spectrum of each fluorophore in the sample.

[0054] The collected light can be measured continuously or at discrete intervals. In some cases, the method includes measuring the light continuously. In other cases, the light is measured at discrete intervals, such as measuring light every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds, and every 1000 milliseconds, or some other interval.

[0055] Measurement of the collected light may be performed one or more times in the subject methods, such as two or more times, such as three or more times, such as five or more times, and such as ten or more times, In certain embodiments, the light from the sample is measured two or more times, and in certain instances the data is averaged.

[0056] The light from the sample may be measured at one or more wavelengths, for example 5 or more different wavelengths, for example 10 or more different wavelengths, for example 25 or more different wavelengths, for example 50 or more different wavelengths, for example 100 or more different wavelengths, for example 200 or more different wavelengths, for example 300 or more different wavelengths, including measuring light collected at 400 or more different wavelengths.

[0057] In some embodiments, the method includes further conditioning the light from the sample before detecting it. For example, the light from the sample source may pass through one or more lenses, mirrors, pinholes, slits, gratings, optical refractors, and any combination thereof. In some cases, the collected light passes through one or more focusing lenses, for example, to reduce the light profile. In other cases, the emitted light from the sample passes through one or more collimators to reduce the divergence of the light beam.

[0058] In embodiments, light is detected in one or more photodetector channels. In some cases, light from the sample is detected in multiple photodetector channels, such as two or more photodetector channels, such as four or more photodetector channels, such as eight or more photodetector channels, such as sixteen or more photodetector channels, such as thirty-two or more photodetector channels, such as sixty-four or more photodetector channels, such as one hundred and twenty-eight or more photodetector channels, and such as two hundred and fifty-six or more photodetector channels.

[0059] When performing the subject methods according to certain embodiments, a data signal generated in response to light detected by a light detection system is normalized by detector gain. The data signal is normalized, in some cases, by dividing the data signal by the gain to generate a gain-independent normalized data signal. In some cases, the gain used to generate the gain-independent normalized data signal is determined by determining a target gain that provides a predetermined mean fluorescence intensity. In some embodiments, the baseline data signal is used to generate a lookup table for gain control to ensure that changes in mean fluorescence intensity are linearly related to changes in detector settings (e.g., gain settings). In some cases, the method applies settings to each photodetector channel such that mean fluorescence intensity is linearly related to changes in detector gain. In some cases, the method includes performing measurements of a reference light source (e.g., an LED or other light source described above) at different gain or voltage settings to derive a lookup table such that particle mean fluorescence intensity increases linearly with detector gain or voltage setting. In some cases, the method includes determining the photodetector gain setting as a function of the photodetector channel mean fluorescence intensity. In some cases, the method includes determining a detector gain setting for the photodetector sufficient to produce a mean fluorescence intensity that increases linearly with detector gain.

[0060] In some cases, the method includes determining a target predetermined mean fluorescence intensity (MFI). In some cases, the method includes determining a gain setting in one or more photodetector channels that achieves the target predetermined mean fluorescence intensity. In some cases, the method includes determining the mean fluorescence intensity at a plurality of different gain settings. In some embodiments, the method includes changing the gain setting of each photodetector by, for example, 0.01 dB or more, for example, 0.05 dB or more, for example, 0.1 dB or more, for example, 0.5 dB or more, for example, 1 dB or more, for example, 2 dB or more, for example, 3 dB or more, for example, 5 dB or more, and for example, 10 dB or more. In one example, the method includes gradually increasing the gain setting of each photodetector and determining a detector gain setting for the photodetector sufficient to produce an average fluorescence intensity at each operating voltage, for example, gradually increasing the operating voltage by 0.01 dB or more, for example 0.05 dB or more, for example 0.1 dB or more, for example 0.5 dB or more, for example 1 dB or more, for example 2 dB or more, for example 3 dB or more, for example 5 dB or more, and for example 10 dB or more.

[0061] In some cases, the method includes collecting data signals for two or more different gain settings, for example, three or more different gain settings, for example, five or more different gain settings, for example, ten or more different gain settings, for example, twenty-five or more different gain settings, including collecting data signals at fifty or more different gain settings.

[0062] In some cases, the predetermined mean fluorescence intensity is determined by illuminating the reference particles with a light source and detecting fluorescence from the reference particles. In some cases, the reference particles are multispectral fluorescent beads. In particular cases, the multispectral fluorescent beads include a fluorescent dye moiety covalently bound to the particle. In some embodiments, the particles are beads for use in flow cytometry, e.g., having diameters in the nanometer to micrometer range, e.g., 0.01 to 1,000 μm, e.g., 0.1 to 100 μm, e.g., 1 to 100 μm, and e.g., about 1 to 10 μm. Such particles may be of any shape, and in some cases are approximately spherical. Such particles may be made of any suitable material (or combination thereof), including, but not limited to, polymers such as polystyrene; polystyrene containing other copolymers such as divinylbenzene; polymethyl methacrylate (PMMA); polyvinyl toluene (PVT); copolymers such as styrene / butadiene, styrene / vinyl toluene; latex; glass; or other materials, e.g., silica (e.g., SiO2). In some embodiments, the particles of interest are particles with low or no autofluorescence, for example beads such as glass beads.

[0063] In some embodiments, the beads are metal-organic polymer matrices, e.g., organic polymer matrices having a framework structure comprising a metal such as aluminum, barium, antimony, calcium, chromium, copper, erbium, germanium, iron, lead, lithium, phosphorus, potassium, silicon, tantalum, tin, titanium, vanadium, zinc, or zirconium. In some embodiments, the porous metal-organic matrix is ​​an organosiloxane polymer, including, but not limited to, polymers of methyltrimethoxysilane, dimethyldimethoxysilane, tetraethoxysilane, methacryloxypropyltrimethoxysilane, bis(triethoxysilyl)ethane, bis(triethoxysilyl)butane, bis(triethoxysilyl)pentane, bis(triethoxysilyl)hexane, bis(triethoxysilyl)heptane, bis(triethoxysilyl)octane, and combinations thereof.

[0064] The particles of interest can be porous or non-porous. In some embodiments, the particles are non-porous. In other embodiments, the particles are porous, e.g., particles having pores with diameters in the range of 0.01 nm to 1000 nm, e.g., 0.05 nm to 750 nm, e.g., 0.1 nm to 500 nm, e.g., 0.5 nm to 250 nm, e.g., 1 nm to 100 nm, e.g., 5 nm to 75 nm, including particles having pores with diameters in the range of 10 nm to 50 nm.

[0065] In certain embodiments, fluorescently labeled beads of interest include, but are not limited to, fluorescently labeled polystyrene beads, fluorescein beads, rhodamine beads, and other beads tagged with fluorescent dyes. Further examples of fluorescently labeled beads are described in U.S. Patent Nos. 6,350,619, 7,738,094, and 8,248,597, the disclosures of each of which are incorporated herein by reference in their entirety.

[0066] In some embodiments, the scaling factor adjusts the gain-normalized data signal to a predetermined mean fluorescence intensity. In some cases, the scaling factor is an on-target gain that adjusts the gain-normalized data signal to a target mean fluorescence intensity (e.g., determined using multispectral reference particles). In some cases, the scaling factor provides consistent mean fluorescence intensity scaling regardless of detector setting, such as when the mean fluorescence intensity varies by 10% or less, e.g., 9% or less, e.g., 8% or less, e.g., 7% or less, e.g., 6% or less, e.g., 5% or less, e.g., 4% or less, e.g., 3% or less, e.g., 2% or less, e.g., 1% or less, e.g., 0.5% or less, e.g., 0.1% or less, and when the mean fluorescence intensity varies by 0.01% or less. In some cases, the scaling factor provides detector setting-independent mean fluorescence intensity scaling. In certain embodiments, the gain-normalized data signal is multiplied by the scaling factor to generate the scaled data signal.

[0067] In some embodiments, the method includes calculating a scaling factor. In some cases, calculating the scaling factor includes determining a linear gain as a function of the photodetector voltage, determining a gain corresponding to a predetermined mean fluorescence intensity, and calculating a scaling factor that adjusts the generated gain-normalized data signal to the predetermined mean fluorescence intensity. In certain embodiments, the scaling factor is calculated in real time. In certain cases, the scaling factor is applied to the gain-independent data signal in real time. In certain cases, the scaling factor is recalculated after each sample run. In some cases, the scaling factor is stored in software or hardware (e.g., an integrated circuit, e.g., a field programmable gate array). In certain cases, the scaling factor is adjusted to account for tolerances of the particle analyzer. In some cases, the scaling factor is adjusted using a calibration factor, such as a calibration factor that adjusts for system changes, system noise, light source noise, photodetector noise, and particle velocity fluctuations.

[0068] In some embodiments, the method includes adjusting the gain-normalized data signal with a calibration factor. In some cases, the calibration factor adjusts the gain-normalized data signal in response to changes in particle velocity in the flow stream. In some cases, the calibration factor adjusts the gain-normalized data signal in response to changes in laser intensity of the light source.

[0069] In some embodiments, the method includes spectrally unmixing the gain-normalized data signal and adjusting the spectrally unmixed data signal with a scaling factor to generate a scaled unmixed data signal. In these embodiments, the method may include spectrally unmixing light from each fluorophore in the sample (e.g., using a weighted least squares algorithm or a generalized least squares algorithm). In some embodiments, the overlap between each different fluorophore is determined, and the contribution of each fluorophore to the overlapping fluorescence is calculated. In some embodiments, spectrally unmixing the light includes calculating a spectral unmixing matrix of the fluorescence spectra for each of multiple fluorophores having overlapping fluorescence in the sample detected by the light detection system. For example, spectrally unmixing light according to the methods described herein may include a Moore-Penrose inverse or pseudoinverse of the spectral matrix. In some cases, the algorithm for spectral unmixing is characterized by a Cholesky decomposition of the unmixing matrix. In some embodiments, the unmixed spectral data signal is scaled with the detector gain using a spillover matrix. In some cases, the method includes scaling the unmixed data with the gain according to the following:

[0070] 1) Reference gain level G 0 The first spillover matrix M is obtained from the spectrum acquired in 0 where M is an m-by-n matrix, with each column normalized to its maximum value.

[0071] 2) Generate a new spillover matrix M in the active payoff G.

[0072]

number

[0073]

number

[0074] M(G) is used for separation at gain setting G.

[0075] 3) Scale the separated data by S so that the separated mean fluorescence intensity (MFI) is maintained across gain settings. where: f scaled =S×f unscaled and f unscaled is the separation output from the hardware for use in sorting, f scaled is a separate output for use by software.

[0076] In some cases, spectrally resolving the light from each fluorophore (e.g., calculating a spectral separation matrix for each fluorophore) can be used to estimate the abundance of each fluorophore in the sample. In certain embodiments, the abundance of each fluorophore associated with a target particle can be determined. The abundance of each fluorophore associated with a target particle can be used in identifying and classifying the particle. In some cases, the identified or classified particles can be used to sort target particles (e.g., cells) in the sample. In certain embodiments, spectrally resolving the fluorophores in the sample, such as by calculating spectral separation, is performed so that the sorting is fast enough to sort particles in real time after detection by a light detection system.

[0077] In certain embodiments, the gain-normalized data signal is spectrally separated by a separation algorithm such as those described in U.S. Patent No. 11,009,400, U.S. Patent Application No. 18 / 537,103, filed December 12, 2023, and U.S. Provisional Patent Application No. 63 / 622,370, filed January 18, 2024, the disclosures of which are incorporated herein by reference.

[0078] FIG. 1A shows a flowchart for normalizing a gain-independent analyte data signal, according to certain embodiments. In step 101, light from particles of a sample in a flowstream is detected using a light detection system having a photodetector. In some cases, the light detection system includes multiple photodetectors. In step 102, a data signal is generated in response to the detected light. The light from the particles in the flowstream can be detected in multiple different photodetector channels. In step 103, the data signal is normalized by a detector gain to generate a gain-normalized data signal. The gain-normalized data signal, in some embodiments, is gain-independent so that the mean fluorescence intensity is the same regardless of the photodetector settings. In step 104, the gain-normalized data signal is adjusted by a scaling factor to generate a scaled data signal. In some cases, the scaling factor adjusts the gain-normalized data signal to a target mean fluorescence intensity. In some cases, the target mean fluorescence intensity is predetermined using reference particles, such as multispectral beads.

[0079] FIG. 1B shows a step-by-step diagram of generating scaled gain-normalized data, according to certain embodiments. In step 111, a baseline is derived to derive a lookup table for gain control to ensure linear mean fluorescence intensity as detector settings change. Periodic quality control (e.g., daily), shown in step 112, increases the detector gain setting to make the mean fluorescence intensity of the reference particles (e.g., multispectral beads) equal to the on-target gain. The on-target gain and on-target MFI are used to calculate an offset used to adjust the user gain. In step 113, gain-normalized data is generated by dividing the data signal by the linear gain, resulting in gain-normalized data (as shown in step 113, where multispectral bead data exhibits the same MFI regardless of detector setting). The on-target gain derived in step 112 can be utilized as a scaling factor rather than an offset. Unlike calculating an offset, which is applied to saved settings and updated only after quality control, this scaling factor allows the scaling of the MFI to be consistent regardless of detector setting. This can be done independently of detector settings. In certain embodiments, periodic gain settings (e.g., daily gain settings) can be optimized to ensure consistency in sensitivity, resolution, or dynamic range. In some cases, gain settings are left unchanged. Figure 1C shows a comparison of scatter plot analyses of sample data generated using gain-dependent and gain-independent (gain-normalized) data signals, according to certain embodiments.

[0080] FIG. 1D shows a diagram of scaling data signals from a flow cytometer, according to certain embodiments. Scaling data signals from an optical detection system can include three distinct components: 1) detector calibration, 2) periodic (e.g., daily) quality control, and 3) user data acquisition from samples. The detector calibration component includes determining a detector gain for normalizing the data signal and calculating a scaling factor as described above. In some cases, detector calibration includes determining a target mean fluorescence intensity using reference particles (e.g., multispectral beads) and a controlled light source, such as a light-emitting diode (LED). In some cases, the target mean fluorescence intensity is determined to calculate a scaling factor used to adjust the gain-normalized data signal. The quality control component includes adjustments to the system to optimize resolution and reduce noise in the generated data signal. In some cases, the quality control component is performed one or more times per day, e.g., twice per day. In some cases, the quality control component is performed one or more times per week, e.g., twice per week. In embodiments, the quality control component includes detector gain adjustment, ensuring and maintaining resolution consistency (e.g., the resolution of the data signal varies by no more than 15%, for example no more than 14%, for example no more than 13%, for example no more than 12%, for example no more than 11%, for example no more than 10%, for example no more than 9%, for example no more than 8%, for example no more than 7%, for example no more than 6%, for example no more than 5%, for example no more than 4%, for example no more than 3%, for example no more than 2%, for example no more than 1%, for example no more than 0.5%, for example no more than 0.1%, and for example no more than 0.01%). Gain normalization factors may also be generated in the quality control component, as well as fluorophore calibration factors. When sample data is generated, user settings can be adjusted for consistent resolution, data signals can be adjusted using spectral effect parameters, and data signals can be separated (e.g., using a weighted least squares algorithm or a generalized least squares algorithm).The separation data may also be adjusted using calibration factors, such as a calibration factor that adjusts the gain-normalized data signal in response to changes in particle velocity in the flow stream or a calibration factor that adjusts the gain-normalized data signal in response to changes in laser intensity of the light source.

[0081] FIG. 1E illustrates the use of scaled and unscaled data signals in accordance with certain embodiments. As illustrated in FIG. 1E, the spectrally separated but unscaled data signals are used in hardware, such as an integrated circuit device (e.g., an FPGA). In certain cases, the integrated circuit device includes a sorting block that applies the unscaled separated data signals to generate particle sorting decisions using a particle sorter, as described in more detail below. The unscaled separated data can also be scaled in software (e.g., using a processor having memory with instructions stored thereon, as described below). The scaled separated data can be plotted (e.g., on a scatter plot) and gated. The determined gates can be unscaled and provided to the sorting block as described above, or the identified gates can be used for analysis, exported to a display, or the like. FIG. 1F illustrates a comparison of data analysis with and without gain scaling in accordance with certain embodiments.

[0082] In some cases, the sample analyzed in this method is a biological sample. The term "biological sample" is used in its conventional sense and refers to a whole organism, a whole plant, a whole fungus, or a subset of animal tissues, cells, or component parts, and in certain cases may be found in blood, mucus, lymph, synovial fluid, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, amniotic fluid, amniotic fluid, umbilical cord blood, urine, vaginal fluid, and semen. Thus, a "biological sample" refers to both an intact organism or a subset of its tissues, as well as homogenates, lysates, or extracts prepared from an organism or a subset of its tissues, including, but not limited to, plasma, serum, cerebrospinal fluid, lymph, skin, sections of the respiratory tract, gastrointestinal tract, cardiovascular, and genitourinary tract, tears, saliva, milk, blood cells, tumors, and organs. A biological sample can be any type of biological tissue, including both healthy and diseased tissues (e.g., cancerous, malignant, necrotic, etc.). In certain embodiments, the biological sample is a liquid sample such as blood or a derivative thereof, e.g., plasma, tears, urine, semen, etc., and in some cases the sample is a blood sample, including whole blood, such as blood obtained from venipuncture or finger stick (which may or may not be combined with any reagents, such as preservatives, anticoagulants, etc., prior to assay).

[0083] In certain embodiments, the source of the sample is a "mammal" or "mammalian," a term used broadly to describe organisms belonging to the class Mammalia, including the orders Carnivora (e.g., dogs and cats), Rodentia (e.g., mice, guinea pigs, and rats), and Primates (e.g., humans, chimpanzees, and monkeys). In some cases, the subject is a human. The present methods are applicable to samples obtained from human subjects of both genders and at any stage of development (i.e., newborn, infant, juvenile, adolescent, adult), and in certain embodiments, the human subject is a juvenile, adolescent, or adult. While the present disclosure is applicable to samples from human subjects, it will be understood that the methods may also be performed on samples from other animal subjects (i.e., "non-human subjects"), such as, but not limited to, birds, mice, rats, dogs, cats, livestock, and horses.

[0084] Cells of interest can be targeted for characterization according to various parameters, such as phenotypic characteristics identified through the attachment of specific fluorescent labels to the cells of interest. In some embodiments, the system is configured to deflect analyzed droplets determined to contain target cells. A variety of cells can be characterized using the subject methods. Target cells of interest include, but are not limited to, stem cells, T cells, dendritic cells, B cells, granulocytes, leukemia cells, lymphoma cells, viral cells (e.g., HIV cells), NK cells, macrophages, monocytes, fibroblasts, epithelial cells, endothelial cells, and erythroid cells. Target cells of interest include cells bearing favorable cell surface markers or antigens that can be captured or labeled by favorable affinity agents or conjugates thereof. For example, target cells may comprise cell surface antigens such as CD11b, CD123, CD14, CD15, CD16, CD19, CD193, CD2, CD25, CD27, CD3, CD335, CD36, CD4, CD43, CD45RO, CD56, CD61, CD7, CD8, CD34, CD1c, CD23, CD304, CD235a, T cell receptor alpha / beta, T cell receptor gamma / delta, CD253, CD95, CD20, CD105, CD117, CD120b, Notch4, Lgr5 (N-terminus), SSEA-3, TRA-1-60 antigen, disialoganglioside GD2, and CD71. In some embodiments, the target cells are selected from HIV-containing cells from whole blood, bone marrow or umbilical cord blood, Treg cells, antigen-specific T cell populations, tumor cells or hematopoietic progenitor cells (CD34+).

[0085] In practicing the subject methods according to certain embodiments, a volume of an initial fluid sample is injected into a flow cytometer. The volume of sample injected into the particle sorting module can vary, for example, with the sample ranging from 0.001 mL to 1000 mL, for example, from 0.005 mL to 900 mL, for example, from 0.01 mL to 800 mL, for example, from 0.05 mL to 700 mL, for example, from 0.1 mL to 600 mL, for example, from 0.5 mL to 500 mL, for example, from 1 mL to 400 mL, for example, from 2 mL to 300 mL, and for example, from 5 mL to 100 mL.

[0086] In some embodiments, the method includes counting and, optionally, sorting labeled particles (e.g., target cells) in a sample. In performing the subject methods, a fluid sample containing particles is first introduced into a flow nozzle of the system. Upon exiting the flow nozzle, the particles pass substantially one at a time through a sample interrogation region, where each of the particles is illuminated by a light source, and measurements of light scattering parameters, and in some cases, desired fluorescence emissions (e.g., measurements of two or more light scattering parameters and one or more fluorescence emissions), are recorded separately for each particle. Depending on the characteristics of the flow stream being interrogated, the light may be illuminated into 0.001 mm or more of the flow stream, e.g., 0.005 mm or more, e.g., 0.01 mm or more, e.g., 0.05 mm or more, e.g., 0.1 mm or more, e.g., 0.5 mm or more, including 1 mm or more of the flow stream. In certain embodiments, the method includes illuminating a planar cross-section of the flow stream within the sample interrogation region, such as with a laser (as described above). In other embodiments, the method includes illuminating a predetermined length of the flow stream within the sample interrogation region, for example, a length corresponding to the illumination profile of a diffuse laser beam or lamp.

[0087] In certain embodiments, the method comprises irradiating the flow stream at or near the flow cell nozzle orifice. For example, the method can comprise irradiating the flow stream at a location about 0.001 mm or more, e.g., 0.005 mm or more, e.g., 0.01 mm or more, e.g., 0.05 mm or more, e.g., 0.1 mm or more, e.g., 0.5 mm or more, and e.g., 1 mm or more, from the nozzle orifice. In certain embodiments, the method comprises irradiating the flow stream immediately adjacent to the flow cell nozzle orifice.

[0088] In embodiments of the present method, detectors such as photomultiplier tubes (PMTs) are used to record the light passing through each particle (in certain cases referred to as forward light scatter), the light reflected perpendicular to the direction of the particle's flow through the detection region (in some cases referred to as orthogonal or side light scatter), and, if the particle is labeled with a fluorescent marker, the fluorescence emitted by the particle as it passes through the detection region and is illuminated by an energy source. Forward light scatter (FSC), side scatter (SSC), and fluorescence emission each comprise a separate parameter for each particle (or each "event"). Thus, for example, two, three, or four parameters can be collected (and recorded) from particles labeled with two different fluorescent markers. The data recorded for each particle can be analyzed in real time or, if desired, stored in a data storage and analysis means, such as a computer.

[0089] In certain embodiments, particles are detected and uniquely identified by exposing them to excitation light and measuring the fluorescence of each particle in one or more detection channels as needed.The fluorescence emitted by the detection channels used to identify particles and their associated binding complexes can be measured after excitation by a single light source, or can be measured separately after excitation by separate light sources.When separate excitation light sources are used to excite particle labels, the labels can be selected so that all labels can be excited by each of the excitation light sources used.

[0090] In certain embodiments, the method also includes data acquisition, analysis, and recording using a computer or the like, where multiple data channels record data from each detector for light scattering and fluorescence emitted by each particle as it passes through the sample interrogation region of the particle sorting module. In these embodiments, the analysis includes classifying and counting particles so that each particle is represented as a set of digitized parameter values. The system of interest can be configured to trigger on selected parameters to distinguish particles of interest from background and noise. "Trigger" refers to a preset threshold for the detection of a parameter and can be used as a means to detect the passage of a particle through a light source. Detection of an event exceeding the threshold for the selected parameter triggers the acquisition of light scattering and fluorescence data for the particle. Data is not acquired for particles or other components in the medium being assayed that cause a response below the threshold. The trigger parameter can be the detection of forward scattered light caused by the particle passing through the light beam. The flow cytometer then detects and collects the light scattering and fluorescence data of the particle.

[0091] Specific subpopulations of interest are then further analyzed by "gating" based on the data collected for the entire population. To select the appropriate gate, the data is plotted to obtain the best possible subpopulation separation. This procedure can be performed by plotting forward light scatter (FSC) versus side (i.e., orthogonal) light scatter (SSC) on a two-dimensional dot plot. A subpopulation of particles (i.e., those cells within the gate) is then selected, and particles not within the gate are excluded. If desired, a gate can be selected by drawing a line around the desired subpopulation using a cursor on the computer screen. Only those particles within the gate are then further analyzed by plotting other parameters of these particles, such as fluorescence. If desired, the above analysis can be configured to result in counting the particles of interest in the sample.

[0092] The subject methods may further include using the particles in research, laboratory testing, or therapy. In some embodiments, the subject methods include obtaining individual cells prepared from a biological sample of a target fluid or target tissue. For example, the subject methods include obtaining cells from a fluid or tissue sample used as a research or diagnostic specimen for a disease such as cancer. Similarly, the subject methods include obtaining cells from a fluid or tissue sample used for therapy. Cell therapy protocols are protocols in which viable cellular material, including, for example, cells and tissue, can be prepared and introduced into a subject as a therapeutic treatment. Conditions that can be treated by administering flow cytometry-sorted samples include, but are not limited to, blood disorders, immune system disorders, organ damage, and the like.

[0093] A typical cell therapy protocol may include the following steps: sample collection, cell isolation, genetic modification, culture and in vitro expansion, cell harvesting, sample volume reduction and washing, biopreservation, storage, and cell introduction into a subject. The protocol may begin with collecting viable cells and viable tissue from a subject's source tissue to generate a cell and / or tissue sample. The sample may be collected by any suitable procedure, including, for example, administering a cell mobilizing agent to the subject, withdrawing blood from the subject, removing bone marrow from the subject, etc. After collecting the sample, cell enrichment may be performed by several methods, including, for example, centrifugation-based methods, filter-based methods, elution, magnetic separation, fluorescence-activated cell sorting (FACS), etc. In some cases, the enriched cells may be genetically modified by any convenient method, such as nuclease-mediated gene editing. The genetically modified cells may be cultured, activated, and expanded in vitro. In some cases, the cells are preserved, e.g., cryopreserved, and stored for future use, in which the cells are thawed and then administered to a patient, e.g., the cells may be infused into a patient.

[0094] system Aspects of the present disclosure also include systems for implementing the subject methods, for analyzing analyte data, e.g., by generating a gain-normalized data signal. The system, according to certain embodiments, comprises a light source configured to irradiate a sample having particles in a flowstream, a light detection system having a photodetector for detecting light from the illuminated particles, and a processor, the processor comprising a memory operatively coupled to the processor and having instructions stored in the memory that, when executed by the processor, cause the processor to generate a data signal in response to the detected light, normalize the data signal by a detector gain to generate a gain-normalized data signal, and adjust the gain-normalized data signal by a scaling factor to generate a scaled data signal.

[0095] The system may include a display and an operator input device. The operator input device may be, for example, a keyboard, a mouse, etc. The processing module includes a processor that accesses a memory in which instructions for performing the steps of the subject method are stored. The processing module may include an operating system, a graphical user interface (GUI) controller, a system memory, a memory storage device, an input / output controller, a cache memory, a data backup unit, and many other devices. The processor may be a commercially available processor or one of other processors that are or become available. The processor executes an operating system, which interfaces with firmware and hardware in well-known ways and facilitates the processor's coordination and execution of the functions of various computer programs, which may be written in various programming languages, such as Java, Perl, C++, Python, other high-level or low-level languages, and combinations thereof, as known in the art. The operating system typically cooperates with the processor to coordinate and execute the functions of the other components of the computer. The operating system also provides scheduling, input / output control, file and data management, memory management, and communication control and related services, all in accordance with known techniques. In some embodiments, the processor includes analog electronics that provide feedback control, such as negative feedback control.

[0096] The system memory may be any of a variety of known or future memory storage devices. Examples include any commonly available random access memory (RAM), magnetic media such as a resident hard disk or tape, optical media such as a read-and-write compact disc, a flash memory device, or other memory storage device. The memory storage device may be any of a variety of known or future devices, including a compact disc drive, tape drive, or diskette drive. Such types of memory storage devices typically read from and / or write to a program storage medium (not shown), such as a compact disc. Any of these program storage media, or other program storage media now in use or that may later be developed, may be considered a computer program product. As will be appreciated, these program storage media typically store computer software programs and / or data. Computer software programs, also referred to as computer control logic, are typically stored in the system memory and / or in program storage devices used in conjunction with the memory storage devices.

[0097] In some embodiments, a computer program product is described that includes a computer-usable medium having stored thereon control logic (a computer software program including program code). The control logic, when executed by a processor of a computer, causes the processor to perform the functions described herein. In other embodiments, some functions are implemented primarily in hardware, for example, using hardware state machines. Implementing a hardware state machine to perform the functions described herein will be apparent to one skilled in the art.

[0098] The subject programmable logic may be implemented in any of a variety of devices, such as a specifically programmed event processing computer, a wireless communication device, an integrated circuit device, etc. In some embodiments, the programmable logic may be executed by a specifically programmed processor, which may include one or more processors, such as one or more digital signal processors (DSPs), configurable microprocessors, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other equivalent integrated or discrete logic circuits. Combinations of computing devices, such as a DSP with a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration in at least partial data connection, may implement one or more of the features described herein.

[0099] The memory may be any suitable device from which the processor can store and retrieve data, such as a magnetic, optical, or solid-state storage device (including a magnetic or optical disk, or tape, or RAM, or any other suitable device, fixed or portable). The processor may include a general-purpose digital microprocessor that is appropriately programmed from a computer-readable medium carrying the necessary program code. The programming may be provided to the processor remotely through a communications channel or may be pre-stored in a computer program product, such as memory or some other portable or fixed computer-readable storage medium that uses any of these devices in conjunction with memory. For example, a magnetic or optical disk may carry the program and can be read by a disk writer / reader. The system of the present disclosure also includes programming in the form of a computer program product, e.g., algorithms for use in implementing the above-described methods. Programming according to the present disclosure may be recorded on a computer-readable medium, e.g., any medium that can be directly read and accessed by a computer. Such media include, but are not limited to, magnetic storage media such as floppy disks, hard disk storage media and magnetic tape, optical storage media such as CD-ROM, electrical storage media such as RAM, ROM, portable flash drives, and hybrids of these categories such as magnetic / optical storage media.

[0100] The processor may also have access to a communication channel for communicating with a remote user, where remote means that the user is not in direct contact with the system but relays input information to the input manager from an external device, such as a computer connected to a wide area network ("WAN"), telephone network, satellite network, or any other suitable communication channel, including a mobile phone (i.e., smartphone).

[0101] In some embodiments, a system according to the present disclosure may be configured to include a communications interface. In some embodiments, the communications interface includes a receiver and / or a transmitter for communicating with a network and / or another device. The communications interface may be configured for wired or wireless communications, including, but not limited to, radio frequency (RF) communications (e.g., radio frequency identification (RFID), Zigbee communications protocol, Wi-Fi, infrared, wireless universal serial bus (USB), ultra-wideband (UWB), Bluetooth® communications protocol, and cellular communications such as code division multiple access (CDMA) or global system for mobile communications (GSM).

[0102] In one embodiment, the communication interface is configured to include one or more communication ports, e.g., a physical port or interface such as a USB port, a USB-C port, an RS-232 port, or any other suitable electrical connection port that enables data communication between the subject system and other external devices, such as a computer terminal (e.g., in a doctor's office or hospital environment) configured for similar complementary data communication.

[0103] In one embodiment, the communication interface is configured for infrared communication, Bluetooth® communication, or any other suitable wireless communication protocol to enable the target system to communicate with computer terminals and / or other devices such as networks, communication-enabled mobile phones, personal digital assistants, or any other communication device that a user may use in conjunction with.

[0104] In one embodiment, the communication interface is configured to provide connectivity for data transfer using Internet Protocol (IP) over a cellular network, Short Message Service (SMS), a wireless connection to a personal computer (PC) in a local area network (LAN) connected to the Internet, or a Wi-Fi connection to the Internet at a Wi-Fi hotspot.

[0105] In one embodiment, the target system is configured to wirelessly communicate with a server device via a communications interface using common standards such as, for example, 802.11 or Bluetooth® RF protocols, or the IrDA infrared protocol. The server device can be another portable device, such as a smartphone, personal digital assistant (PDA), or notebook computer, or a larger device, such as a desktop computer, appliance, etc. In some embodiments, the server device has a display, such as a liquid crystal display (LCD), and input devices, such as buttons, a keyboard, a mouse, or a touchscreen.

[0106] In some embodiments, the communication interface is configured to automatically or semi-automatically communicate data stored in the target system, e.g., the optional data storage unit, with a network device or a server device using one or more of the communication protocols and / or mechanisms described above.

[0107] The output controller may include a controller for any of a variety of known display devices for presenting information to a user, whether human or machine, local or remote. When a display device provides visual information, this information may typically be logically and / or physically organized as an array of pixels. A graphical user interface (GUI) controller provides a graphical input / output interface between the system and the user and may include any of a variety of known or future software programs for processing user input. The functional elements of the computer may communicate with each other via a system bus. Some of these communications may be achieved in alternative embodiments using a network or other type of remote communication. The output manager may also provide information generated by the processing modules to a remote user, for example, via the Internet, telephone, or satellite network, according to known techniques. Presentation of data by the output manager may be performed according to various known techniques. As some examples, the data may include SQL, HTML, or XML documents, email or other files, or data in other formats. The data may also include Internet URL addresses so that the user can retrieve additional SQL, HTML, XML, or other documents or data from remote sources. The one or more platforms present in the subject system can be any type of known or future-developed computer platform, but they are typically computers of a class commonly referred to as servers. However, they may also be mainframe computers, workstations, or other computer types. They may be connected via any known or future type of cabling or other communication system, including wireless systems, and may or may not be networked. They may be co-located or physically separated.In some cases, various operating systems may be used for any computer platform, depending on the type and / or manufacturer of the computer platform selected. Suitable operating systems include Windows NT, Windows XP, Windows 7, Windows 8, Windows 10, iOS, macOS, Linux, Ubuntu, Fedora, OS / 400, i5 / OS, IBM i, Android, SGI IRIX, Oracle Solaris, and the like.

[0108] In some embodiments, the system further includes a flow cytometer operably connected to the processor. Flow cytometers of interest generally include a flow cell. A flow cell of interest includes a cuvette configured to transport particles in a flow stream. As discussed herein, "flow cell" is described in its conventional sense, referring to a component that includes a flow channel for a liquid flow stream for transporting particles of sheath fluid. A cuvette of interest has a passageway (i.e., a flow channel) therethrough. The flow stream of which the flow channel is configured may include a liquid sample injected from a sample tube. In certain cases, the flow cell includes an optically accessible flow channel. The cuvette may be constructed of, for example, quartz, glass, clear plastic, or the like. In some embodiments, the cuvette is formed from silica, such as fused silica. In some cases, the flow cell is configured to be illuminated with light from a light source at one or more interrogation points. As discussed herein, "interrogation point" refers to an area within the flow cell where particles are illuminated by light from a light source, for example, for analysis. The size of the interrogation point may vary as needed. For example, if 0 μm represents the optical axis of the light emitted by the light source, the interrogation point can range from -50 μm to 50 μm, such as from -25 μm to 40 μm, and such as from -15 μm to 30 μm. Depending on specific considerations (e.g., the number and placement of lasers), multiple illumination points may be present within the flow cell.

[0109] In some embodiments, the flow cell includes or is configured for use with a sample injection port configured to provide a sample to the flow cell, hi embodiments, the sample injection system is configured to provide a suitable flow of sample to the flow cell internal chamber (i.e., flow channel). Depending on the desired characteristics of the flow stream, the rate of sample delivered by the sample injection port to the flow cell chamber may be 1 μL / min or more, for example 2 μL / min or more, for example 3 μL / min or more, for example 5 μL / min or more, for example 10 μL / min or more, for example 15 μL / min or more, for example 25 μL / min or more, for example 50 μL / min or more, and for example 100 μL / min or more, and in some cases the rate of sample delivered by the sample injection port to the flow cell chamber is 1 μL / sec or more, for example 2 μL / sec or more, for example 3 μL / sec or more, for example 5 μL / sec or more, for example 10 μL / sec or more, for example 15 μL / sec or more, for example 25 μL / sec or more, for example 50 μL / sec or more, and for example 100 μL / sec or more.

[0110] The sample injection port may be an orifice disposed in the wall of the internal chamber or a conduit disposed at the proximal end of the internal chamber. When the sample injection port is an orifice disposed in the wall of the internal chamber, the sample injection port orifice may have any suitable cross-sectional shape, including, but not limited to, rectilinear cross-sectional shapes such as square, rectangular, trapezoidal, triangular, and hexagonal, curvilinear cross-sectional shapes such as circular and elliptical, and irregular shapes such as a parabolic bottom joined to a flat top. In certain embodiments, the sample injection port has a circular orifice. The size of the sample injection port orifice may vary depending on the shape, in certain cases ranging from 0.1 mm to 5.0 mm, e.g., 0.2 mm to 3.0 mm, e.g., 0.5 mm to 2.5 mm, e.g., 0.75 mm to 2.25 mm, e.g., 1 mm to 2 mm, and e.g., 1.25 mm to 1.75 mm, e.g., a 1.5 mm opening.

[0111] In certain cases, the sample injection port is a conduit located at the proximal end of the flow cell internal chamber. For example, the sample injection port may be a conduit positioned so that the orifice of the sample injection port is aligned with the flow cell orifice. When the sample injection port is a conduit aligned with the flow cell orifice, the cross-sectional shape of the sample injection tube may be any suitable shape, including, but not limited to, linear cross-sectional shapes such as square, rectangular, trapezoidal, triangular, and hexagonal, curved cross-sectional shapes such as circular and elliptical, and irregular shapes such as a parabolic bottom joined to a flat top. In certain cases, the orifice of the conduit may have an opening ranging from 0.1 mm to 5.0 mm, e.g., 0.2 mm to 3.0 mm, e.g., 0.5 mm to 2.5 mm, e.g., 0.75 mm to 2.25 mm, e.g., 1 mm to 2 mm, and e.g., 1.25 mm to 1.75 mm, e.g., 1.5 mm, depending on the shape. The shape of the tip of the sample injection port may be the same as or different from the cross-sectional shape of the sample injection tube. For example, the orifice of the sample injection port may include a beveled tip having a bevel angle in the range of 1° to 10°, such as 2° to 9°, such as 3° to 8°, such as 4° to 7°, and for example 5°.

[0112] In some embodiments, the flow cell also includes a sheath fluid injection port configured to provide sheath fluid to the flow cell. In embodiments, the sheath fluid injection system is configured to provide a flow of sheath fluid to the flow cell interior chamber, e.g., in conjunction with the sample, to generate a stacked sheath fluid flow stream surrounding the sample flow stream. Depending on the desired characteristics of the flow stream, the velocity of the sheath fluid delivered by the sheath fluid injection port to the flow cell chamber can be 25 μL / sec or more, e.g., 50 μL / sec or more, e.g., 75 μL / sec or more, e.g., 100 μL / sec or more, e.g., 250 μL / sec or more, e.g., 500 μL / sec or more, e.g., 750 μL / sec or more, e.g., 1000 μL / sec or more, and e.g., 2500 μL / sec or more.

[0113] In some embodiments, the sheath fluid injection port is an orifice disposed in the wall of the internal chamber. The sheath fluid injection port orifice may be of any suitable shape, with cross-sectional shapes of interest including, but not limited to, rectilinear cross-sectional shapes such as square, rectangular, trapezoidal, triangular, and hexagonal, curvilinear cross-sectional shapes such as circular and elliptical, and irregular shapes such as a parabolic bottom joined to a flat top. The size of the sheath fluid injection port orifice may vary depending on the shape, with certain cases ranging from 0.1 mm to 5.0 mm, e.g., 0.2 mm to 3.0 mm, e.g., 0.5 mm to 2.5 mm, e.g., 0.75 mm to 2.25 mm, e.g., 1 mm to 2 mm, and e.g., 1.25 mm to 1.75 mm, e.g., having a 1.5 mm opening.

[0114] The disclosed system includes a light source configured to illuminate particles in the flow stream at an interrogation point within the flow cell. The number of light sources within a flow cytometer can vary. In some embodiments, the flow cytometer includes a single light source. Alternatively, the flow cytometer may include multiple light sources in some cases. In some such cases, the number of light sources ranges from 2 to 10, e.g., 2 to 5, and e.g., 2 to 4. Any convenient light source can be used as the light source described herein. In some embodiments, the light source is a laser. In embodiments, the laser can be any convenient laser, such as a continuous wave laser. For example, the laser can be a diode laser, such as an ultraviolet diode laser, a visible diode laser, or a near-infrared diode laser. In other embodiments, the laser can be a helium-neon (HeNe) laser. In some cases, the laser is a gas laser such as a helium-neon laser, an argon laser, a krypton laser, a xenon laser, a nitrogen laser, a CO laser, a CO laser, an argon-fluorine (ArF) excimer laser, a krypton-fluorine (KrF) excimer laser, a xenon-chlorine (XeCl) excimer laser, or a xenon-fluorine (XeF) excimer laser, or a combination thereof. In other cases, the flow cytometer of interest includes a dye laser such as a stilbene, coumarin, or rhodamine laser. In still other cases, the laser of interest includes a metal vapor laser such as a helium-cadmium (HeCd) laser, a helium-mercury (HeHg) laser, a helium-selenium (HeSe) laser, a helium-silver (HeAg) laser, a strontium laser, a neon-copper (NeCu) laser, a copper laser, or a gold laser, and combinations thereof. In still other instances, the subject flow cytometers include solid-state lasers such as ruby ​​lasers, Nd:YAG lasers, NdCrYAG lasers, Er:YAG lasers, Nd:YLF lasers, Nd:YVO4 lasers, Nd:YCa4O(BO3)3 lasers, Nd:YCOB lasers, titanium sapphire lasers, thulium YAG lasers, ytterbium YAG lasers, ytterbium2O3 lasers, or cerium-doped lasers, and combinations thereof.

[0115] The laser light source according to certain embodiments may also include one or more optical adjustment components. In certain embodiments, the optical adjustment component may include any device located between the light source and the flow cell that can change the spatial width of the illumination or some other characteristic of the illumination from the light source, such as the illumination direction, wavelength, beam width, beam intensity, and focus. The optical adjustment protocol may include any convenient device that adjusts one or more characteristics of the light source, including, but not limited to, lenses, mirrors, filters, optical fibers, wavelength separators, pinholes, slits, collimation protocols, and combinations thereof. In certain embodiments, the target flow cytometer includes one or more focusing lenses. In one example, the focusing lens may be a reduction lens. In yet other embodiments, the target flow cytometer includes optical fibers.

[0116] In some embodiments, the system is a non-laser light source, such as a lamp, including but not limited to a halogen lamp, a deuterium arc lamp, a xenon arc lamp, a light emitting diode, such as a broadband LED with a continuous spectrum, a superluminescent light emitting diode, a semiconductor light emitting diode, a wide spectrum LED white light source, a multi-LED integrated white light source, etc. In some cases, the non-laser light source is a stabilized fiber coupled broadband light source, a white light source, or any combination thereof, among other light sources.

[0117] The light source can be positioned at any suitable distance from the flow cell, for example, the light source and the flow cell are separated by 0.005 mm or more, for example, 0.01 mm or more, for example, 0.05 mm or more, for example, 0.1 mm or more, for example, 0.5 mm or more, for example, 1 mm or more, for example, 5 mm or more, for example, 10 mm or more, for example, 25 mm or more, and for example, 100 mm or more. Furthermore, the light source can be positioned at any suitable angle relative to the flow cell, for example, in the range of 10 to 90 degrees, for example, 15 to 85 degrees, for example, 20 to 80 degrees, for example, 25 to 75 degrees, and for example, 30 to 60 degrees, for example, 90 degrees.

[0118] In some embodiments, the intended light source includes multiple lasers configured to provide laser light for discrete illumination of the flowstream, e.g., two or more lasers, e.g., three or more lasers, e.g., four or more lasers, e.g., five or more lasers, e.g., ten or more lasers, and e.g., fifteen or more lasers configured to provide laser light for discrete illumination of the flowstream. Depending on the desired wavelength of light for illuminating the flowstream, each laser may have a specific wavelength that varies from 200 nm to 1500 nm, e.g., 250 nm to 1250 nm, e.g., 300 nm to 1000 nm, e.g., 350 nm to 900 nm, and e.g., 400 nm to 800 nm. In certain embodiments, the intended lasers may include one or more of a 405 nm laser, a 488 nm laser, a 561 nm laser, and a 635 nm laser.

[0119] In certain embodiments, the light source is an optical beam generator configured to generate two or more frequency-shifted optical beams. In some cases, the optical beam generator includes a laser and a radio-frequency generator configured to apply a radio-frequency drive signal to an acousto-optic device to generate two or more angularly deflected laser beams. In these embodiments, the laser can be a pulsed laser or a continuous-wave laser. For example, the laser in the optical beam generator of interest can be a gas laser such as a helium-neon laser, an argon laser, a krypton laser, a xenon laser, a nitrogen laser, a CO laser, a CO laser, an argon-fluorine (ArF) excimer laser, a krypton-fluorine (KrF) excimer laser, a xenon-chlorine (XeCl) excimer laser, a xenon-fluorine (XeF) excimer laser, or a combination thereof; a dye laser such as a stilbene, coumarin, or rhodamine laser; a helium-cadmium (HeCd) laser, a helium-mercury (HeHg) laser, a helium-selenium (HeS) laser, or a combination thereof. e) metal vapor lasers such as helium-silver (HeAg) lasers, strontium lasers, neon-copper (NeCu) lasers, copper lasers or gold lasers, and combinations thereof; solid state lasers such as ruby ​​lasers, Nd:YAG lasers, NdCrYAG lasers, Er:YAG lasers, Nd:YLF lasers, Nd:YVO4 lasers, Nd:YCa4O(BO3)3 lasers, Nd:YCOB lasers, titanium sapphire lasers, thulium YAG lasers, ytterbium YAG lasers, ytterbium2O3 lasers or cerium doped lasers, and combinations thereof.

[0120] The acousto-optical device may be any convenient acousto-optical device configured to frequency-shift laser light using applied acoustic waves. In certain embodiments, the acousto-optical device is an acousto-optical deflector. The acousto-optical device in the target system is configured to generate an angularly deflected laser beam from light from a laser and an applied high-frequency drive signal. The high-frequency drive signal may be applied to the acousto-optical device using any suitable high-frequency drive signal source, such as a direct digital synthesizer (DDS), an arbitrary waveform generator (AWG), or an electrical pulse generator.

[0121] In an embodiment, the controller is configured to apply high frequency drive signals to the acousto-optic device to generate a desired number of angularly deflected laser beams within the output laser beam, for example, configured to apply three or more high frequency drive signals, for example, four or more high frequency drive signals, for example, five or more high frequency drive signals, for example, six or more high frequency drive signals, for example, seven or more high frequency drive signals, for example, eight or more high frequency drive signals, for example, nine or more high frequency drive signals, for example, ten or more high frequency drive signals, for example, fifteen or more high frequency drive signals, for example, twenty-five or more high frequency drive signals, for example, fifty or more high frequency drive signals, including configured to apply one hundred or more high frequency drive signals.

[0122] In some cases, to generate an angularly deflected laser beam intensity profile within the output laser beam, the controller is configured to apply a high frequency drive signal having an amplitude that varies, for example, from about 0.001 V to about 500 V, for example, from about 0.005 V to about 400 V, for example, from about 0.01 V to about 300 V, for example, from about 0.05 V to about 200 V, for example, from about 0.1 V to about 100 V, for example, from about 0.5 V to about 75 V, for example, from about 1 V to about 50 V, for example, from about 2 V to about 40 V, for example, from 3 V to about 30 V, and for example, from about 5 V to about 25 V. In some embodiments, each applied high frequency drive signal has a frequency of about 0.001 MHz to about 500 MHz, for example, about 0.005 MHz to about 400 MHz, for example, about 0.01 MHz to about 300 MHz, for example, about 0.05 MHz to about 200 MHz, for example, about 0.1 MHz to about 100 MHz, for example, about 0.5 MHz to about 90 MHz, for example, about 1 MHz to about 75 MHz, for example, about 2 MHz to about 70 MHz, for example, about 3 MHz to about 65 MHz, for example, about 4 MHz to about 60 MHz, and for example, about 5 MHz to about 50 MHz.

[0123] In certain embodiments, the controller includes a processor having a memory operatively coupled to the processor, the memory having instructions stored therein that, when executed by the processor, cause the processor to generate an output laser beam having an angularly deflected laser beam with a desired intensity profile. For example, the memory may include instructions for generating two or more, e.g., three or more, e.g., four or more, e.g., five or more, e.g., ten or more, e.g., twenty-five or more, e.g., fifty or more, angularly deflected laser beams having the same intensity, including the memory may include instructions for generating one hundred or more angularly deflected laser beams having the same intensity. In other embodiments, the memory may include instructions for generating two or more, e.g., three or more, e.g., four or more, e.g., five or more, e.g., ten or more, e.g., twenty-five or more, e.g., fifty or more, angularly deflected laser beams having different intensities, including the memory may include instructions for generating one hundred or more angularly deflected laser beams having different intensities.

[0124] In certain embodiments, the controller includes a processor having a memory operatively coupled to the processor, the memory having instructions stored therein that, when executed by the processor, cause the processor to generate an output laser beam that increases in intensity from the edge to the center of the output laser beam along a horizontal axis. In these cases, the intensity of the angularly deflected laser beam at the center of the output beam may be in the range of 0.1% to about 99%, e.g., 0.5% to about 95%, e.g., 1% to about 90%, e.g., about 2% to about 85%, e.g., about 3% to about 80%, e.g., about 4% to about 75%, e.g., about 5% to about 70%, e.g., about 6% to about 65%, e.g., about 7% to about 60%, e.g., about 8% to about 55%, including about 10% to about 50% of the intensity of the angularly deflected laser beam at the edge of the output laser beam along the horizontal axis. In other embodiments, the controller includes a processor having a memory operatively coupled to the processor, the memory having instructions stored therein that, when executed by the processor, cause the processor to generate an output laser beam that increases in intensity from the edge to the center of the output laser beam along a horizontal axis. In these cases, the intensity of the angularly deflected laser beam at the edge of the output beam may be in the range of 0.1% to about 99%, such as 0.5% to about 95%, such as 1% to about 90%, such as about 2% to about 85%, such as about 3% to about 80%, such as about 4% to about 75%, such as about 5% to about 70%, such as about 6% to about 65%, such as about 7% to about 60%, such as about 8% to about 55%, including about 10% to about 50% of the intensity of the angularly deflected laser beam at the center of the output laser beam along the horizontal axis. In yet another embodiment, the controller comprises a processor having a memory operatively coupled to the processor, the memory having instructions stored therein that, when executed by the processor, cause the processor to generate an output laser beam having an intensity profile with a Gaussian distribution along a horizontal axis.In yet another embodiment, the controller comprises a processor having a memory operatively coupled to the processor, the memory having instructions stored therein that, when executed by the processor, cause the processor to generate an output laser beam having a top-hat intensity profile along a horizontal axis.

[0125] In embodiments, the objective optical beam generator can be configured to generate spatially separated angularly deflected laser beams within the output laser beam. Depending on the applied high frequency drive signal and the desired illumination profile of the output laser beam, the angularly deflected laser beams can be separated by 0.001 μm or more, e.g., 0.005 μm or more, e.g., 0.01 μm or more, e.g., 0.05 μm or more, e.g., 0.1 μm or more, e.g., 0.5 μm or more, e.g., 1 μm or more, e.g., 5 μm or more, e.g., 10 μm or more, e.g., 100 μm or more, e.g., 500 μm or more, e.g., 1000 μm or more, and e.g., 5000 μm or more. In some embodiments, the system is configured to generate angularly deflected laser beams within the output laser beam that overlap with adjacent angularly deflected laser beams along the horizontal axis of the output laser beam, such as by 0.001 μm or more, e.g., 0.005 μm or more, e.g., 0.01 μm or more, e.g., 0.05 μm or more, e.g., 0.1 μm or more, e.g., 0.5 μm or more, e.g., 1 μm or more, e.g., 5 μm or more, e.g., 10 μm or more, e.g., 100 μm or more, e.g., 5000 μm or more. The overlap between adjacent angularly deflected laser beams (e.g., beam spot overlap) can be an overlap of 0.001 μm or more, such as an overlap of 0.005 μm or more, for example an overlap of 0.01 μm or more, for example an overlap of 0.05 μm or more, for example an overlap of 0.1 μm or more, for example an overlap of 0.5 μm or more, for example an overlap of 1 μm or more, for example an overlap of 5 μm or more, for example an overlap of 10 μm or more, and for example an overlap of 100 μm or more.

[0126] In certain cases, the optical beam generator configured to generate two or more frequency-shifted optical beams includes a laser excitation module as described in U.S. Patent Nos. 9,423,353, 9,784,661, and 10,006,852, and U.S. Patent Application Publication Nos. 2017 / 0133857 and 2017 / 0350803, the disclosures of which are incorporated herein by reference.

[0127] The flow cytometer further includes a detector configured to collect light emitted by the illuminated particles. The photodetector is configured to detect the particle-modulated light carried by the fiber optic light collection element and generate a signal based on a characteristic (e.g., intensity) of the light. For example, the one or more particle-modulated light detectors may include one or more side-scattered light detectors for detecting side-scattered wavelengths of light (i.e., light refracted and reflected from the surface and internal structure of the particle). In some embodiments, the flow cytometer includes a single side-scattered light detector. In other embodiments, the flow cytometer includes multiple, e.g., two or more, e.g., three or more, e.g., four or more, and e.g., five or more, side-scattered light detectors.

[0128] Any convenient detector for detecting collected light can be used in the side-scattered light detector described herein. Detectors of interest can include, but are not limited to, optical sensors or detectors such as active pixel sensors (APS), avalanche photodiodes, image sensors, charge-coupled devices (CCDs), intensified charge-coupled devices (ICCDs), light-emitting diodes, photon counters, bolometers, pyroelectric detectors, photoresistors, photocells, photodiodes, photomultiplier tubes (PMTs), phototransistors, quantum dot photoconductors, or photodiodes, and combinations thereof, among other detectors. In certain embodiments, the collected light is measured with a charge-coupled device (CCD), a semiconductor charge-coupled device (CCD), an active pixel sensor (APS), a complementary metal-oxide semiconductor (CMOS) image sensor, or an N-type metal-oxide semiconductor (NMOS) image sensor. In certain embodiments, the detector has a resolution of 0.01 cm. 2 ~10cm 2 , e.g. 0.05cm 2 ~9cm 2 , e.g. 0.1cm 2 ~8cm 2 , e.g. 0.5cm 2 ~7cm 2 , and e.g. 1 cm 2 ~5cm 2 and a photomultiplier tube having an active detection surface area in each region in the range of 1000 nm to 1000 nm.

[0129] In embodiments, a subject flow cytometer also includes a fluorescence detector configured to detect one or more fluorescent wavelengths of light, hi other embodiments, the flow cytometer includes multiple, e.g., two or more, e.g., three or more, e.g., four or more, five or more, ten or more, fifteen or more, and e.g., twenty or more, fluorescence detectors.

[0130] Any convenient detector for detecting collected light can be used in the fluorescence detectors described herein. Detectors of interest can include, but are not limited to, optical sensors or detectors such as active pixel sensors (APS), avalanche photodiodes, image sensors, charge-coupled devices (CCDs), intensified charge-coupled devices (ICCDs), light-emitting diodes, photon counters, bolometers, pyroelectric detectors, photoresistors, photocells, photodiodes, photomultiplier tubes (PMTs), phototransistors, quantum dot photoconductors, or photodiodes, and combinations thereof, among other detectors. In certain embodiments, collected light is measured with a charge-coupled device (CCD), a semiconductor charge-coupled device (CCD), an active pixel sensor (APS), a complementary metal-oxide semiconductor (CMOS) image sensor, or an N-type metal-oxide semiconductor (NMOS) image sensor. In certain embodiments, the detector has a resolution of 0.01 cm. 2 ~10cm 2 , e.g. 0.05cm 2 ~9cm 2 , e.g. 0.1cm 2 ~8cm 2 , e.g. 0.5cm 2 ~7cm 2 , and e.g. 1 cm 2 ~5cm 2 and a photomultiplier tube having an active detection surface area in each region in the range of 1000 nm to 1000 nm.

[0131] When a subject flow cytometer includes multiple fluorescence detectors, each fluorescence detector may be the same, or the collection of fluorescence detectors may be a combination of different types of detectors. For example, when a subject flow cytometer includes two fluorescence detectors, in some embodiments, the first fluorescence detector is a CCD-type device and the second fluorescence detector (or imaging sensor) is a CMOS-type device. In other embodiments, both the first fluorescence detector and the second fluorescence detector are CCD-type devices. In still other embodiments, both the first fluorescence detector and the second fluorescence detector are CMOS-type devices. In still other embodiments, the first fluorescence detector is a CCD-type device and the second fluorescence detector is a photomultiplier tube (PMT). In still other embodiments, the first fluorescence detector is a CMOS-type device and the second fluorescence detector is a photomultiplier tube. In still other embodiments, both the first fluorescence detector and the second fluorescence detector are photomultiplier tubes.

[0132] In embodiments of the present disclosure, the subject fluorescence detector is configured to measure collected light at one or more wavelengths, e.g., two or more wavelengths, e.g., five or more different wavelengths, e.g., ten or more different wavelengths, e.g., twenty-five or more different wavelengths, e.g., fifty or more different wavelengths, e.g., one hundred or more different wavelengths, e.g., two or more different wavelengths, e.g., two or more different wavelengths, e.g., three hundred or more different wavelengths, including measuring light emitted by a sample in the flowstream at four hundred or more different wavelengths. In some embodiments, two or more detectors in a module described herein are configured to measure the same or overlapping wavelengths of collected light.

[0133] In some embodiments, the intended fluorescence detector is configured to measure light collected over a range of wavelengths (e.g., 200 nm to 1000 nm). In certain embodiments, the intended detector is configured to collect a spectrum of light over a range of wavelengths. For example, a flow cytometer may include one or more detectors configured to collect a spectrum of light over one or more wavelength ranges from 200 nm to 1000 nm. In still other embodiments, the intended detector is configured to measure light emitted by a sample in a flow stream at one or more specific wavelengths. For example, a module may include one or more detectors configured to measure light at one or more of the following wavelengths: 450 nm, 518 nm, 519 nm, 561 nm, 578 nm, 605 nm, 607 nm, 625 nm, 650 nm, 660 nm, 667 nm, 670 nm, 668 nm, 695 nm, 710 nm, 723 nm, 780 nm, 785 nm, 647 nm, 617 nm, and any combination thereof. In certain embodiments, one or more detectors may be configured to pair with a particular fluorophore, such as one used with a sample in a fluorescence assay.

[0134] The flow cytometer may include any suitable mechanism for providing sheath fluid and sample fluid to the sample fluid input coupler and sheath fluid input coupler. For example, the sample fluid input coupler may be fluidly connected to a sample fluid line (e.g., tubing) that is fluidly connected to a sample fluid reservoir. Similarly, the sheath fluid input coupler may be fluidly connected to a sheath fluid line that is fluidly connected to a sheath fluid reservoir. Similarly, the flow cytometer may include any suitable mechanism for managing waste from the flow stream. The fluid output coupler may be fluidly connected to a waste line that is fluidly connected to a waste reservoir. A fluid management system that may be adapted for use with a subject flow cytometer is provided in U.S. Patent Application Publication No. 2022 / 0341838, the disclosure of which is incorporated herein by reference in its entirety.

[0135] In some cases, the system is configured to detect light by the light detection system on multiple photodetector channels, such as two or more photodetector channels, such as four or more photodetector channels, such as eight or more photodetector channels, such as 16 or more photodetector channels, such as 32 or more photodetector channels, such as 64 or more photodetector channels, such as 128 or more photodetector channels, such as 256 or more photodetector channels, and such as 512 or more photodetector channels.

[0136] In some embodiments, the memory includes instructions for normalizing a data signal generated in response to light detected by the light detection system by a detector gain. In some cases, the memory includes instructions for normalizing the data signal by dividing the data signal by the gain to generate a gain-independent normalized data signal. In some cases, the memory includes instructions for determining a gain used to generate the gain-independent normalized data signal. In some cases, the memory includes instructions for determining this gain by determining a target gain that provides a predetermined mean fluorescence intensity. In some embodiments, the memory includes instructions for generating a lookup table for gain control using a baseline data signal to ensure that changes in mean fluorescence intensity are linearly related to changes in detector settings (e.g., gain settings). In some cases, the memory includes instructions for applying settings to each photodetector channel such that mean fluorescence intensity is linearly related to changes in detector gain. In some cases, the memory includes instructions for performing measurements of a reference light source (e.g., an LED or other light source described above) at different gain or voltage settings to derive a lookup table such that particle mean fluorescence intensity increases linearly with detector gain or voltage setting. In some cases, the memory includes instructions for determining a photodetector gain setting as a function of a photodetector channel mean fluorescence intensity, hi some cases, the memory includes instructions for determining a detector gain setting for the photodetector sufficient to produce a mean fluorescence intensity that increases linearly with detector gain.

[0137] In some cases, the memory includes instructions for determining a target predetermined mean fluorescence intensity (MFI). In some cases, the memory includes instructions for determining gain settings in one or more photodetector channels that achieve the target predetermined mean fluorescence intensity. In some cases, the memory includes instructions for determining mean fluorescence intensities at a plurality of different gain settings. In some embodiments, the memory includes instructions for changing the gain setting of each photodetector by, for example, 0.01 dB or more, for example, 0.05 dB or more, for example, 0.1 dB or more, for example, 0.5 dB or more, for example, 1 dB or more, for example, 2 dB or more, for example, 3 dB or more, for example, 5 dB or more, and for example, 10 dB or more. In one example, the memory includes instructions for gradually increasing the gain setting of each photodetector and instructions for determining a detector gain setting for the photodetector sufficient to produce an average fluorescence intensity at each operating voltage, for example, gradually increasing the operating voltage by 0.01 dB or more, for example 0.05 dB or more, for example 0.1 dB or more, for example 0.5 dB or more, for example 1 dB or more, for example 2 dB or more, for example 3 dB or more, for example 5 dB or more, and for example 10 dB or more.

[0138] In some cases, the memory includes instructions for acquiring data signals for two or more different gain settings, for example, three or more different gain settings, for example, five or more different gain settings, for example, ten or more different gain settings, for example, twenty-five or more different gain settings, and includes instructions for acquiring data signals at fifty or more different gain settings.

[0139] In some cases, the scaling factor is an on-target gain that adjusts the gain-normalized data signal to a target mean fluorescence intensity (e.g., determined using multispectral reference particles). In some cases, the scaling factor provides consistent mean fluorescence intensity scaling regardless of detector setting, such as when the mean fluorescence intensity varies by 10% or less, e.g., 9% or less, e.g., 8% or less, e.g., 7% or less, e.g., 6% or less, e.g., 5% or less, e.g., 4% or less, e.g., 3% or less, e.g., 2% or less, e.g., 1% or less, e.g., 0.5% or less, e.g., 0.1% or less, and when the mean fluorescence intensity varies by 0.01% or less. In some cases, the scaling factor provides mean fluorescence intensity scaling that is independent of detector setting. In certain embodiments, the memory includes instructions for multiplying the gain-normalized data signal by the scaling factor to generate the scaled data signal.

[0140] In some embodiments, the memory includes instructions for calculating a scaling factor that adjusts the gain-normalized data signal to a predetermined mean fluorescence intensity. In some cases, the memory includes instructions for calculating the scaling factor by determining a linear gain as a function of photodetector voltage, determining a gain corresponding to a predetermined mean fluorescence intensity, and calculating a scaling factor that adjusts the generated gain-normalized data signal to the predetermined mean fluorescence intensity. In certain embodiments, the memory includes instructions for calculating the scaling factor in real time. In certain cases, the memory includes instructions for applying the scaling factor to the gain-independent data signal in real time. In certain cases, the memory includes instructions for recalculating the scaling factor after each sample is run. In some cases, the memory includes instructions for storing the scaling factor in software or hardware (e.g., an integrated circuit, such as a field programmable gate array). In certain cases, the memory includes instructions for adjusting the scaling factor to account for tolerances of the particle analyzer. In some cases, the memory includes instructions for adjusting the scaling factor using a calibration factor, such as a calibration factor that adjusts for system changes, system noise, light source noise, photodetector noise, and particle velocity fluctuations.

[0141] In some embodiments, the memory includes instructions for adjusting the gain-normalized data signal with a calibration factor. In some cases, the calibration factor adjusts the gain-normalized data signal in response to changes in particle velocity in the flow stream. In some cases, the calibration factor adjusts the gain-normalized data signal in response to changes in laser intensity of the light source.

[0142] In some embodiments, the memory includes instructions for spectrally unmixing the gain-normalized data signal and adjusting the spectrally unmixed data signal with a scaling factor to generate a scaled unmixed data signal. In these embodiments, the memory includes instructions for spectrally unmixing light from each fluorophore in the sample (e.g., using a weighted least squares algorithm or a generalized least squares algorithm). In some embodiments, overlap between each different fluorophore is determined, and the contribution of each fluorophore to the overlapping fluorescence is calculated. In some embodiments, the memory includes instructions for spectrally unmixing the light by calculating a spectral unmixing matrix of the fluorescence spectra for each of multiple fluorophores having overlapping fluorescence in the sample detected by the light detection system. For example, the memory includes instructions for spectrally unmixing the light according to the methods described herein using a Moore-Penrose inverse or pseudoinverse of the spectral matrix. In some cases, the algorithm for spectral unmixing is characterized by a Cholesky decomposition of the unmixing matrix. In some embodiments, the unmixed spectral data signal is scaled with detector gain using a spillover matrix. In some cases, the memory includes instructions for scaling the unmixed data with gain according to the following:

[0143] 4) Reference gain level G 0 The first spillover matrix M is obtained from the spectrum acquired in 0 where M is an m-by-n matrix, with each column normalized to its maximum value.

[0144] 5) Generate a new spillover matrix M in the active payoff G.

[0145]

number

[0146] where: The left-multiplied diagonal matrix scales each row of M by the gain ratio corresponding to that detector; The right-multiplied diagonal matrix scales each column of M by its maximum value, ensuring that each column has values ​​in [0,1];

[0147]

number

[0148] M(G) is used for separation at gain setting G.

[0149] 6) Scale the separated data by S so that the separated mean fluorescence intensity (MFI) is maintained across gain settings. where: f scaled =S×f unscaled and f unscaled is the separation output from the hardware for use in sorting, f scaled is a separate output for use by software.

[0150] In some cases, the memory includes instructions for spectrally resolving light from each fluorophore (e.g., calculating a spectral separation matrix for each fluorophore) for use in estimating the abundance of each fluorophore in the sample. In certain embodiments, the memory includes instructions for determining the abundance of each fluorophore associated with a target particle. The abundance of each fluorophore associated with a target particle can be used in identifying and classifying the particle. In some cases, the identified or classified particles can be used to sort target particles (e.g., cells) in the sample. In certain embodiments, spectrally resolving the fluorophores in the sample, such as by calculating spectral separation, is performed such that the sorting is fast enough to sort particles in real time after detection by a light detection system.

[0151] In certain embodiments, the memory includes instructions having a spectral separation algorithm such as those described in U.S. Patent No. 11,009,400, U.S. Patent Application No. 18 / 537,103, filed December 12, 2023, and U.S. Provisional Patent Application No. 63 / 622,370, filed January 18, 2024, the disclosures of which are incorporated herein by reference.

[0152] In some embodiments, the system includes or is operably coupled to a flow cytometer. Suitable flow cytometry systems include those described in Ormerod (ed.), Flow Cytometry: A Practical Approach, Oxford Univ. Press (1997), Jaroszeski et al. (eds.), Flow Cytometry Protocols, Methods in Molecular Biology No. 91, Humana Press (1997), Practical Flow Cytometry, 3rd ed., Wiley-Liss (1995), Virgo, et al. (2012) Ann Clin Biochem. Jan;49(pt 1):17-28, Linden, et al., Semin Thromb Hemost. 2004 Oct;30(5):502-11, Alison, et al. J Pathol, 2010 Dec;222(4):335-344, and Herbig, et al. (2007) Crit Rev Ther Drug Carrier Syst. 24(3):203-255, the disclosures of which are incorporated herein by reference.In certain instances, flow cytometry systems of interest include a BD Biosciences FACSCanto™ flow cytometer, a BD Biosciences FACSCanto™ II flow cytometer, a BD Accuri™ flow cytometer, a BD Accuri™ C6 Plus flow cytometer, a BD Biosciences FACSCelesta™ flow cytometer, a BD Biosciences FACSLyric™ flow cytometer, a BD Biosciences FACSVerse™ flow cytometer, a BD Biosciences FACSymphony™ flow cytometer, a BD Biosciences LSRFortessa™ flow cytometer, a BD Biosciences LSRFortessa™ X-20 flow cytometer, a BD Biosciences FACSPresto™ flow cytometer, a BD Biosciences FACSVia™ flow cytometer, and a BD Biosciences FACSCalibur™ cell sorter, a BD Biosciences FACSCount™ cell sorter, a BD Biosciences These include the FACSLyric™ cell sorter, BD Biosciences Via™ cell sorter, BD Biosciences Influx™ cell sorter, BD Biosciences Jazz™ cell sorter, BD Biosciences Aria™ cell sorter, BD Biosciences FACSAria™ II cell sorter, BD Biosciences FACSAria™ III cell sorter, BD Biosciences FACSAria™ Fusion cell sorter, and BD Biosciences FACSMelody™ cell sorter, BD Biosciences FACSymphony™ S6 cell sorter, BD Biosciences FACSDiscover™ cell sorter, and others.

[0153] In some embodiments, the subject system is a system according to U.S. Patent Nos. 10,663,476, 10,620,111, 10,613,017, 10,605,713, 10,585,031, 10,578,542, 10,578,469, 10,481,074, 10,302,5 No. 45, No. 10,145,793, No. 10,113,967, No. 10,006,852, No. 9,952,076, No. 9,933,341, No. No. 9,726,527, No. 9,453,789, No. 9,200,334, No. 9,097,640, No. 9,095,494, No. 9,092,03 No. 4, No. 8,975,595, No. 8,753,573, No. 8,233,146, No. 8,140,300, No. 7,544,326, No. 7,2 01,875, 7,129,505, 6,821,740, 6,813,017, 6,809,804, 6,372,506, and flow cytometry systems such as those described in US Pat. Nos. 5,700,692, 5,643,796, 5,627,040, 5,620,842, 5,602,039, 4,987,086, and 4,498,766, the disclosures of which are incorporated herein by reference in their entireties.

[0154] In some embodiments, the flow cytometer is configured as an imaging flow cytometer.For example, in certain cases, the target system is a flow cytometry system configured to image particles in a flow stream by fluorescence imaging using radiofrequency tagged emission (FIRE), such as those described in Diebold, et al. Nature Photonics Vol.7(10), 806-810(2013), and U.S. Patent Nos. 9,423,353, 9,784,661 and 10,006,852, and U.S. Patent Application Publication Nos. 2017 / 0133857 and 2017 / 0350803, the disclosures of which are incorporated herein by reference.In some embodiments where the flow cytometer is a particle sorter, the particle sorter is an image-enabled particle sorter. Image-enabled particle sorters are described in US Provisional Patent Applications Nos. 63 / 431,803 and 63 / 465,057, the disclosures of which are incorporated herein by reference in their entireties.

[0155] FIG. 2 illustrates a system 200 for flow cytometry according to an exemplary embodiment of the present disclosure. The system 200 includes a laser 201 configured to illuminate particles 211 in a flow stream 214 at an interrogation point 215 within a flow cell 210. While the example of FIG. 2 shows a single laser, it is understood that multiple lasers can also be used. The laser beam from the laser 201 is directed to a focusing lens 202, which focuses the beam onto a portion of the fluid stream where the sample particles 211 are located within the flow cell 210. The flow cell 210 is part of a fluid system that directs particles in the stream, typically one at a time, into the focused laser beam for interrogation. Alternatively, if the flow cytometer is a stream-in-air cytometer, a nozzle top can be used.

[0156] As shown in FIG. 2 , flow cell 210 is fluidly connected to sheath fluid reservoir 203 containing sheath fluid and sample fluid reservoir 204 containing sample fluid. Sheath fluid from sheath fluid reservoir 203 is provided to at least one sheath fluid injection port 208 via a conduit (i.e., sheath fluid line) 207. Additionally, sample fluid containing particles 211 from sample fluid reservoir 204 is provided to sample injection port 206 via a conduit (i.e., sample fluid line) 205. Sample injection port 206 is fluidly connected to a sample injector 213 (e.g., a sample injection needle) configured to introduce particles 211 into the interior of flow cell 210. Particles 211 are hydrodynamically focused via sheath fluid entering sheath fluid injection port 208 such that flow stream 214 is formed downstream of tapered portion 212 of flow cell 210. Particles emitting at the distal end of flow cell 210 can be disposed of and / or collected via any suitable protocol. For example, depending on the type of flow cytometry being performed, the particles may be collected at the distal end of the flow cell 210, for example, via a waste line. Alternatively, the particles may be sorted.

[0157] Light from the laser beam interacts with particles 211 in the sample through diffraction, refraction, reflection, scattering, and absorption, with re-emitted light at a variety of different wavelengths depending on particle characteristics such as particle size, internal structure, and the presence of one or more fluorescent molecules attached to or naturally present on or within the particle. The fluorescent light and diffracted, refracted, reflected, and scattered light may be sent to one or more detectors. In particular, forward-scattered light (FSC) is sent to a forward-scattered light detector 223. The forward-scattered light detector 223 is positioned slightly off-axis from the direct beam passing through the flow cell 210 and is configured to detect diffracted light, i.e., excitation light traveling primarily in a forward direction through or around the particle. The intensity of the light detected by the forward-scattered light detector 223 depends on the overall size of the particle. The forward-scattered light detector may include, for example, a photodiode. An optical filter 221a and a scattering bar 222 are positioned between the forward-scattered light detector 223 and the forward-scattered light detector 223. The optical filter 221a may be configured to filter out non-FSC light of at least one wavelength, while the scattering bar 222 may be configured to prevent the incident beam from the laser 201 (i.e., non-scattered light) from being detected by the forward scattered light detector 223.

[0158] Additionally, side-scattered light (SSC) is detected by the side-scattered light detector 224. In other words, the side-scattered light detector 224 is configured to detect refracted and reflected light from the surface and internal structure of the particle 211, which tends to increase as particle structures become more complex. In the example of FIG. 2, the flow cytometer 200 includes a dichroic mirror 220a configured to reflect SSC light to the side-scattered light detector 224 and pass non-SSC light (e.g., fluorescence). An optical filter 221b is configured to prevent non-SSC light of at least one wavelength from being detected by the side-scattered light detector 224. Also shown are fluorescence detectors 225a-225c, each configured to detect fluorescence of a different wavelength. For example, the dichroic mirror 220b may be configured to reflect fluorescence (FL) corresponding to a first wavelength (or wavelength range) to the fluorescence detector 225a and pass light of other wavelengths. Optical filter 221c may be configured to prevent light of at least one wavelength that does not correspond to the first wavelength (or wavelength range) from being detected by fluorescence detector 225a. Similarly, dichroic mirror 220c is configured to reflect FL light corresponding to the second wavelength (or wavelength range) to fluorescence detector 225b and pass light of a third wavelength (or wavelength range) to be detected by fluorescence detector 225c. Optical filter 221d is configured to prevent light of at least one wavelength that does not correspond to the second wavelength (or wavelength range) from being detected by fluorescence detector 225b. Furthermore, optical filter 221e is configured to prevent light of at least one wavelength that does not correspond to the third wavelength (or wavelength range) from being detected by fluorescence detector 225c.

[0159] Those skilled in the art will recognize that flow cytometers according to embodiments of the present disclosure are not limited to the flow cytometer shown in FIG. 2 , but may include any flow cytometer known in the art. For example, a flow cytometer can have any number of lasers, beam splitters, filters, and detectors of various wavelengths and in a variety of different configurations. For example, the embodiment of FIG. 2 shows three fluorescence detectors for illustrative purposes, but it will be understood that any suitable number of fluorescence detectors can be used.

[0160] During operation, the operation of the cytometer is controlled by controller / processor 290, and measurement data from the detectors may be stored in memory 295 and processed by controller / processor 290. While not explicitly shown, controller / processor 290 is coupled to the detectors to receive output signals from the detectors, and may also be coupled to the electrical and electromechanical components of the flow cytometer to control laser 201, fluid flow parameters, etc. Input / output (I / O) functionality 297 may also be provided within the system. Memory 295, controller / processor 290, and I / O 297 may be provided entirely as an integral part of the flow cytometer. In such embodiments, a display may also form part of I / O functionality 297 for presenting experimental data to a user of cytometer 200. Alternatively, memory 295 and controller / processor 290 and some or all of the I / O functionality may be part of one or more external devices, such as a general-purpose computer. In some embodiments, some or all of memory 295 and controller / processor 290 may be in wireless or wired communication with cytometer 200. In conjunction with memory 295 and I / O 297, controller / processor 290 can be configured to perform a variety of functions related to the preparation and analysis of flow cytometer experiments.

[0161] Different fluorescent molecules in a panel of fluorescent dyes used in a flow cytometer experiment emit light in their own characteristic wavelength bands. The particular fluorescent labels used in the experiment and their associated fluorescence emission bands can be selected to approximately match the filter window of the detector. I / O 297 can be configured to receive data regarding a flow cytometer experiment having a panel of fluorescent labels and multiple cell populations having multiple markers, with each cell population having a subset of the multiple markers. I / O 297 can also be configured to receive biological data assigning one or more markers to one or more cell populations, marker density data, emission spectrum data, data assigning labels to one or more markers, and cytometer configuration data. Flow cytometer experiment data, such as label spectral characteristics and flow cytometer configuration data, can also be stored in memory 295. Controller / processor 290 can be configured to evaluate one or more assignments of labels to markers.

[0162] In some embodiments, the subject system is a particle sorting system configured to sort particles using an enclosed particle sorting module, such as that described in U.S. Patent Application Publication No. 2017 / 0299493, filed March 28, 2017, the disclosure of which is incorporated herein by reference. In certain embodiments, particles (e.g., cells) of a sample are sorted using a sorting determination module having multiple sorting determination units, such as that described in U.S. Patent Application Publication No. 2020 / 0256781, filed December 23, 2019, the disclosure of which is incorporated herein by reference. In some embodiments, a system for sorting components of a sample includes a particle sorting module with deflection plates, such as that described in U.S. Patent Application Publication No. 2017 / 0299493, filed March 28, 2017, the disclosure of which is incorporated herein by reference.

[0163] In certain embodiments, the system is a fluorescence imaging using a radio frequency tagged luminescence image-enabled particle sorter as shown in FIG. 3. Particle sorter 300 includes a light illumination component 300a, including a light source 301 (e.g., a 488 nm laser) that generates an output light beam 301a, which is split into beams 302a and 302b by a beam splitter 302. Light beam 302a propagates through an acousto-optic device (e.g., an acousto-optic deflector, AOD) 303 to generate an output beam 303a having one or more angularly deflected light beams. In some cases, output beam 303a generated from acousto-optic device 303 includes a local oscillator beam and multiple radio frequency comb beams. Light beam 302b propagates through an acousto-optic device (e.g., an acousto-optic deflector, AOD) 304 to generate an output beam 304a having one or more angularly deflected light beams. In some cases, output beam 304a generated from acousto-optic device 304 includes a local oscillator beam and multiple radio frequency comb beams. Output beams 303a and 304a generated from acousto-optical devices 303 and 304, respectively, are combined with beam splitter 305 to generate output beam 305a, which is conveyed through optical component 306 (e.g., an objective lens) to illuminate particles in flow cell 307. In certain embodiments, acousto-optical device 303 (AOD) splits a single laser beam into an array of beamlets, each having a different optical frequency and angle. A second AOD 304 adjusts the optical frequency of a reference beam, which is then overlapped with the array of beamlets at beam combiner 305. In certain embodiments, the light illumination system having a light source and acousto-optical device can also include those described in Schraivogel, et al. (“High-speed fluorescence image-enabled cell sorting,” Science (2022), 375(6578):315-320) and U.S. Patent Application Publication No. 2021 / 0404943, the disclosures of which are incorporated herein by reference.

[0164] Output beam 305a illuminates sample particles 308 propagating through flow cell 307 (e.g., with sheath fluid 309) in illumination region 310. As shown in illumination region 310, multiple beams (e.g., angularly deflected, high-frequency shifted optical beams shown as dots across illumination region 310) overlap with a reference local oscillator beam (shown as diagonal lines across illumination region 310). Due to their different optical frequencies, the overlapping beams exhibit beating behavior, causing each beamlet to emit a distinct frequency f 1~n carries a sinusoidal modulation.

[0165] Light from the illuminated sample is conveyed to a light detection system 300b, which includes multiple photodetectors. The light detection system 300b includes a forward-scattered light photodetector 311 for generating a forward-scattered image 311a and a side-scattered light photodetector 312 for generating a side-scattered image 312a. The light detection system 300b also includes a bright-field photodetector 313 for generating a light loss image 313a. In some embodiments, the forward-scattered light detector 311 and the side-scattered light detector 312 are photodiodes (e.g., avalanche photodiodes, APDs). In some cases, the bright-field photodetector 313 is a photomultiplier tube (PMT). Fluorescence from the illuminated sample is also detected by fluorescence photodetectors 314-317. In some cases, the photodetectors 314-317 are photomultiplier tubes. The light from the illuminated sample is directed through a beam splitter 320 to the side-scattered light detection channel 312 and the fluorescence detection channels 314-317. Light detection system 300b includes bandpass optical components 321, 322, 323, and 324 (e.g., dichroic mirrors) for transmitting light of predetermined wavelengths to light detectors 314-317. In some cases, optical component 321 is a 534 nm / 40 nm bandpass. In some cases, optical component 322 is a 586 nm / 42 nm bandpass. In some cases, optical component 323 is a 700 nm / 54 nm bandpass. In some cases, optical component 324 is a 783 nm / 56 nm bandpass. The first number represents the center of the spectral band. The second number provides the range of the spectral band. Thus, a 510 / 20 filter extends 10 nm on either side of the center of the spectral band, i.e., from 500 nm to 520 nm.

[0166] Data signals generated in response to light detected in scattered light detection channels 311 and 312, bright-field light detection channel 313, and fluorescence detection channels 314-317 are processed by real-time digital processing by processors 350 and 351. Images 311a-317a can be generated in each light detection channel based on the data signals generated by processors 350 and 351. Image-enabled sorting is performed in response to a sorting signal generated by sorting trigger 352. Sorting component 300c includes deflection plates 331 for deflecting particles into a sample container 332 or to a waste stream 333. In some cases, sorting component 300c is configured to sort particles using an enclosed particle sorting module, such as that described in U.S. Patent Application Publication No. 2017 / 0299493, filed March 28, 2017, the disclosure of which is incorporated herein by reference. In certain embodiments, the sorting component 300c includes a sorting determination module having multiple sorting determination units, such as those described in U.S. Patent Application Publication No. 2020 / 0256781, the disclosure of which is incorporated herein by reference.

[0167] In some embodiments, the system is a particle analyzer, and particle analysis system 401 (FIG. 4) can be used to analyze and characterize particles, with or without physically sorting the particles into a collection vessel. FIG. 4 shows a functional block diagram of a particle analysis system for computation-based sample analysis and particle characterization. In some embodiments, particle analysis system 401 is a flow system. Particle analysis system 401 includes a fluidic system 402. Fluidic system 402 can include or be coupled to a sample tube 405 and a moving fluid column within the sample tube through which particles 403 (e.g., cells) of the sample move along a common sample path 409.

[0168] The particle analysis system 401 includes a detection system 404 configured to collect a signal from each particle as it passes through one or more detection stations along a common sample path. The detection station 408 generally refers to a monitoring area 407 of the common sample path. Detection, in some implementations, can include detecting light or one or more other characteristics of the particle 403 as it passes through the monitoring area 407. In FIG. 4, one detection station 408 is shown with one monitoring area 407. Some implementations of the particle analysis system 401 can include multiple detection stations. Additionally, some detection stations can monitor more than one area.

[0169] Each signal is assigned a signal value to form a data point for each particle. This data may be referred to as event data, as described above. The data points may be multidimensional data points that include values ​​for each property measured for the particle. The detection system 404 is configured to collect such data points continuously over a first time interval.

[0170] The particle analysis system 401 may also include a control system 406. The control system 406 may include one or more processors, amplitude control circuitry, and / or frequency control circuitry. The illustrated control system may be operatively associated with the fluid system 402. The control system may be configured to generate a calculated signal frequency for at least a portion of the first time interval based on the Poisson distribution and the number of data points collected by the detection system 404 during the first time interval. The control system 406 may further be configured to generate an experimental signal frequency based on the number of data points in the portion of the first time interval. The control system 406 may further compare the experimental signal frequency to the calculated signal frequency or a predetermined signal frequency.

[0171] 5 shows a functional block diagram of an example particle analyzer control system for analyzing and displaying biological events, such as an analysis controller (e.g., processor) 500. Analysis controller 500 can be configured to implement various processes for controlling the graphical display of biological events.

[0172] The particle analyzer or sorting system 502 can be configured to acquire biological event data. For example, a flow cytometer can generate flow cytometry event data. The particle analyzer 502 can be configured to provide the biological event data to the analysis controller 500. A data communication channel can be included between the particle analyzer or sorting system 502 and the analysis controller 500. The biological event data can be provided to the analysis controller 500 via the data communication channel. The analysis controller 500 can be a processor configured to perform the methods of the present invention, for example, by applying a distance-based classification model to determine a density distinction threshold in a size-based analyte feature space, applying a density-based clustering algorithm to separate the analyte data into high-density clusters and low-density clusters based on the density threshold, and classifying the analyte data based on the high-density clusters and low-density clusters based on the size-based analyte feature space.

[0173] The analysis controller 500 can be configured to receive biological event data from a particle analyzer or sorting system 502. The biological event data received from the particle analyzer or sorting system 502 can include flow cytometry event data. The analysis controller 500 can be configured to provide a graphical display on a display device 506 that includes a first plot of the biological event data. The analysis controller 500 can be further configured to render a region of interest, for example, as a gate around a population of the biological event data shown by the display device 506, overlaid on the first plot. In some embodiments, the gate can be a logical combination of one or more graphical regions of interest depicted in a histogram or bivariate plot of a single parameter. In some embodiments, the display can be used to display particle parameters or saturation detector data.

[0174] Analysis controller 500 can be further configured to display the biological event data within the gate on display device 506 differently from other events within the biological event data outside the gate. For example, analysis controller 500 can be configured to render the color of the biological event data contained within the gate differently from the color of the biological event data outside the gate. Display device 506 can be implemented as a monitor, tablet computer, smartphone, or other electronic device configured to present a graphical interface.

[0175] The analysis controller 500 can be configured to receive a gate selection signal identifying a gate from a first input device. For example, the first input device can be implemented as a mouse 510. The mouse 510 can initiate a gate selection signal to the analysis controller 500 that identifies a gate to be displayed on or operated via the display device 506 (e.g., by clicking the desired gate when a cursor is positioned there). In some implementations, the first device can be implemented as a keyboard 508 or other means for providing input signals to the analysis controller 500, such as a touchscreen, a stylus, an optical detector, or a voice recognition system. Some input devices can include multiple input functions. In such implementations, each input function can be considered an input device. For example, as shown in FIG. 5, the mouse 510 can include a right mouse button and a left mouse button, each capable of generating a trigger event.

[0176] The trigger event can cause the analysis controller 500 to change how the data is displayed, what portions of the data are actually displayed on the display device 506, and / or provide input to further processing, such as selecting a population for particle sorting purposes.

[0177] In some embodiments, the analysis controller 500 can be configured to detect when a gate selection is initiated by the mouse 510. The analysis controller 500 can be further configured to automatically modify the visualization of the plot to facilitate the gating process. The modification can be based on a particular distribution of the biological event data received by the analysis controller 500.

[0178] The analysis controller 500 can be connected to a storage device 504. The storage device 504 can be configured to receive and store biological event data from the analysis controller 500. The storage device 504 can also be configured to receive and store flow cytometry event data from the analysis controller 500. The storage device 504 can be further configured to enable retrieval of biological event data, such as flow cytometry event data, by the analysis controller 500.

[0179] The display device 506 can be configured to receive display data from the analysis controller 500. The display data can include a plot of the biological event data and a gate that delineates a section of the plot. The display device 506 can be further configured to modify the information presented according to input received from the analysis controller 500 in conjunction with input from the particle analyzer 502, the storage device 504, the keyboard 508, and / or the mouse 510.

[0180] In some implementations, the analysis controller 500 can generate a user interface for receiving exemplary events for sorting. For example, the user interface can include controls for receiving exemplary events or exemplary images. The exemplary events or images or exemplary gates can be provided prior to collection of event data for the sample or based on an initial set of events for a subset of the sample.

[0181] FIG. 6A is a schematic diagram of a particle sorter system 600 (e.g., particle analyzer or sorting system 502) according to one embodiment presented herein. In some embodiments, the particle sorter system 600 is a cell sorter system. As shown in FIG. 6A, a droplet-forming transducer 602 (e.g., a piezoelectric oscillator) is coupled to a fluid conduit 601, which can be coupled to, include, or be a nozzle 603. Within the fluid conduit 601, a sheath fluid 604 hydrodynamically focuses a sample fluid 606 containing particles 609 into a moving fluid column 608 (e.g., a stream). Within the moving fluid column 608, the particles 609 (e.g., cells) are aligned in single file across a monitoring area 611 (e.g., where laser streams intersect) illuminated by an illumination source 612 (e.g., a laser). The vibration of droplet forming transducer 602 causes moving fluid column 608 to break up into multiple droplets 610 , some of which contain particles 609 .

[0182] During operation, the detection station 614 (e.g., an event detector) identifies when a particle (or cell) of interest crosses the monitoring area 611. The detection station 614 is fed to a timing circuit 628, which in turn feeds a flash charge circuit 630. At a droplet breakoff point, signaled by a timed droplet delay (Δt), a flash charge can be applied to the moving fluid column 608 so that the droplet of interest carries a charge. The droplet of interest may contain one or more particles or cells to be sorted. The charged droplets can then be sorted by activating a deflection plate (not shown) to deflect the droplets into a collection tube or a container, such as a multi-well or microwell sample plate, where a well or microwell can be associated with the particular droplet of interest. As shown in FIG. 6A, the droplets can be collected in a waste receptacle 638.

[0183] Detection system 616 (e.g., a droplet boundary detector) helps automatically determine the phase of the droplet drive signal as a particle of interest passes through monitoring area 611. An exemplary droplet boundary detector is described in U.S. Patent No. 7,679,039, the entire contents of which are incorporated herein by reference. Detection system 616 enables the instrument to accurately calculate the location of each detected particle in the droplet. Detection system 616 can provide amplitude signal 620 and / or phase signal 618, which are then provided (via amplifier 622) to amplitude control circuit 626 and / or frequency control circuit 624. Amplitude control circuit 626 and / or frequency control circuit 624 then control droplet forming transducer 602. Amplitude control circuit 626 and / or frequency control circuit 624 can be included in a control system.

[0184] In some implementations, the sorting electronics (e.g., detection system 616, detection station 614, and processor 640) can be coupled to a memory configured to store detected events and sorting decisions based thereon. The sorting decisions can be included in the particle's event data. In some implementations, the detection system 616 and detection station 614 can be implemented as a single detection unit or can be communicatively coupled such that event measurements can be collected by either the detection system 616 or the detection station 614 and provided to a non-collecting element.

[0185] FIG. 6B is a schematic diagram of a particle sorter system according to one embodiment presented herein. The particle sorter system 600 shown in FIG. 6B includes deflection plates 652 and 654. An electric charge can be applied via a stream charging wire within the barb. This creates a stream of droplets 610 containing particles 609 for analysis. The particles can be illuminated with one or more light sources (e.g., lasers) to generate light scattering and fluorescence information. The information about the particles is analyzed, such as by sorting electronics or another detection system (not shown in FIG. 6B). Deflection plates 652 and 654 can be independently controlled to attract or repel charged droplets, directing them toward a destination collection receptacle (e.g., one of 672, 674, 676, or 678). 6B, deflector plates 652 and 654 can be controlled to direct particles along a first path 662 toward a receptacle 674 or along a second path 668 toward a receptacle 678. If the particle is not of interest (e.g., does not exhibit scattering or illumination information within a specified sorting range), the deflector plates can allow the particle to continue along flow path 664. Such uncharged droplets can enter a waste receptacle, such as via an aspirator 670.

[0186] Sorting electronics can be included to initiate collection of measurements, receive fluorescent signals for the particles, and determine how to adjust the deflection plates to cause particle sorting. An exemplary implementation of the embodiment shown in Figure 6B includes the BD FACSAria™ line of flow cytometers commercially available from Becton, Dickinson and Company (Franklin Lakes, NJ).

[0187] FIG. 7 illustrates the general architecture of an exemplary computing device 700 according to certain embodiments. The general architecture of computing device 700 illustrated in FIG. 7 includes an arrangement of computer hardware and software components. However, not all of these generally conventional elements need be shown to constitute an enabling disclosure. As illustrated, computing device 700 includes a processing unit 710, a network interface 720, a computer-readable medium drive 730, an input / output device interface 740, a display 750, and input devices 760, all of which may communicate with each other via a communications bus. Network interface 720 may provide a connection to one or more networks or computing systems. Thus, processing unit 710 may receive information and instructions from other computing systems or services via a network. Processing unit 710 also communicates with memory 770 and may further provide output information to optional display 750 via input / output device interface 740. For example, analysis software (e.g., data analysis software or a program such as FlowJo®) stored as executable instructions in the analysis system's non-transitory memory may display flow cytometry event data to a user. The input / output device interface 740 can also accept input from optional input devices 760 such as a keyboard, mouse, digital pen, microphone, touch screen, gesture recognition system, voice recognition system, gamepad, accelerometer, gyroscope, or other input device.

[0188] Memory 770 may include computer program instructions (grouped in some embodiments as modules or components) that processing unit 710 executes to implement one or more embodiments. Memory 770 generally includes RAM, ROM, and / or other persistent, secondary, or non-transitory computer-readable media. Memory 770 may store an operating system 772 that provides computer program instructions used by processing unit 710 in the general management and operation of computing device 700. Data may be stored in data storage device 790. Memory 770 may further include computer program instructions and other information for implementing aspects of the present disclosure.

[0189] Non-transitory computer-readable storage medium Aspects of the present disclosure further include non-transitory computer-readable storage media having instructions for performing the subject methods, such as for performing one or more computer-implemented methods described herein. The computer-readable storage medium may be used by one or more computers to fully or partially automate a system for performing the methods described herein. In certain embodiments, instructions according to the methods described herein may be encoded on a computer-readable medium in the form of "programming," and the term "computer-readable medium" as used herein refers to any non-transitory storage medium involved in providing instructions and data to a computer for execution and processing. Examples of suitable non-transitory storage media include floppy disks, hard disks, optical disks, magneto-optical disks, CD-ROMs, CD-Rs, magnetic tapes, non-volatile memory cards, ROMs, DVD-ROMs, Blu-ray disks, solid-state disks, and network-attached storage devices (NAS), whether such devices are internal or external to the computer. A file containing information may be "stored" on a computer-readable medium, where "storing" refers to recording information so that it can be accessed and retrieved at a later date by a computer. The computer-implemented methods described herein can be performed using programming that can be written in one or more of any number of computer programming languages, including, for example, Python, Java, Java Script, C, C#, C++, Go, R, Swift, PHP, as well as many others.

[0190] In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for detecting light from particles in a sample in a flow stream using a light detection system having a light detector, an algorithm for generating a data signal in response to the detected light, an algorithm for normalizing the data signal by a detector gain to generate a gain-normalized data signal, and an algorithm for adjusting the gain-normalized data signal by a scaling factor to generate a scaled data signal.

[0191] In some cases, the non-transitory computer-readable storage medium includes an algorithm for applying a scaling factor that adjusts the gain-normalized data signal to a predetermined mean fluorescence intensity. In some cases, the non-transitory computer-readable storage medium includes an algorithm for calculating the scaling factor. In some cases, the non-transitory computer-readable storage medium includes an algorithm for determining a linear gain as a function of photodetector voltage, an algorithm for determining a gain that corresponds to a predetermined mean fluorescence intensity, and an algorithm for calculating a scaling factor that adjusts the generated gain-normalized data signal to the predetermined mean fluorescence intensity.

[0192] In some cases, the non-transitory computer-readable storage medium includes an algorithm for deriving a linear gain from a lookup table. In some cases, the non-transitory computer-readable storage medium includes an algorithm for illuminating a photodetector with a light source of multiple different intensities, an algorithm for detecting light from the light sources of multiple different intensities at multiple different photodetector voltages, and an algorithm for determining a detector gain setting for the photodetector sufficient to produce an average fluorescence intensity that increases linearly with detector gain. In some cases, the non-transitory computer-readable storage medium includes an algorithm for determining a predetermined average fluorescence intensity. In some cases, the non-transitory computer-readable storage medium includes an algorithm for illuminating a reference particle (e.g., a multispectral bead) with a light source and an algorithm for detecting fluorescence from the reference particle. In some cases, the non-transitory computer-readable storage medium includes an algorithm for normalizing a data signal using a detector gain, which is the gain of the photodetector at a predetermined average fluorescence intensity. In some cases, the non-transitory computer-readable storage medium includes an algorithm for adjusting the gain-normalized data signal with a calibration factor. In some cases, the non-transitory computer-readable storage medium includes an algorithm for adjusting the gain-normalized data signal with a calibration factor that adjusts the gain-normalized data signal in response to changes in particle velocity in the flow stream. In some cases, the non-transitory computer-readable storage medium includes an algorithm for adjusting the gain-normalized data signal with a calibration factor that adjusts the gain-normalized data signal in response to changes in laser intensity of the light source. In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for spectrally separating the gain-normalized data signal and an algorithm for adjusting the spectrally separated data signal with a scaling factor to generate a scaled separated data signal.

[0193] The non-transitory computer-readable storage medium can be used in one or more computer systems having a display and an operator input device. The operator input device can be, for example, a keyboard, a mouse, etc. The processing module includes a processor that accesses a memory in which instructions for performing the steps of the subject method are stored. The processing module can include an operating system, a graphical user interface (GUI) controller, a system memory, a memory storage device, and an input / output controller, a cache memory, a data backup unit, and many other devices. The processor can be a commercially available processor or one of other processors that are or become available. The processor executes an operating system, which interfaces with firmware and hardware in well-known manners and facilitates the processor's coordination and execution of the functions of various computer programs, which may be written in various programming languages, such as those mentioned above, other high-level or low-level languages, and combinations thereof, as known in the art. The operating system typically cooperates with the processor to coordinate and execute the functions of the other components of the computer. The operating system also provides scheduling, input / output control, file and data management, memory management, and communication control and related services, all in accordance with known techniques.

[0194] kit Aspects of the present disclosure further include kits, which contain storage media such as magneto-optical disks, CD-ROMs, CD-Rs, magnetic tapes, non-volatile memory cards, ROMs, DVD-ROMs, Blu-ray discs, solid-state disks, and network-attached storage devices (NAS). Any of these program storage media, or others now in use or that may later be developed, may be included in the subject kits. In embodiments, the program storage media contain instructions for analyzing flow cytometer data in the methods described herein and for use in the systems described herein. In embodiments, the instructions contained on the computer-readable media provided in the subject kits, or portions thereof, may be implemented as software components of software for analyzing data. In these embodiments, a computer-controlled system according to the present disclosure may function as a software "plug-in" to an existing software package (e.g., FlowJo®).

[0195] In addition to the above components, the subject kits may further include instructions (in some embodiments). These instructions may be present in the subject kits in various forms, one or more of which may be present in the kit. One form in which these instructions may be present is information printed on a suitable medium or substrate, such as one or more sheets of paper on which the information is printed, kit packaging, a package insert, etc. Another form in which these instructions may be present is a computer-readable medium on which the information is recorded, such as a diskette, a compact disc (CD), a portable flash drive, etc. Another form in which these instructions may be present is a website address that can be used via the Internet to access the information at a remote site.

[0196] usefulness The subject methods, systems, and computer systems find use in a variety of applications in which it is desirable to calibrate or optimize optical detection systems (e.g., having multiple optical detectors), such as particle analyzers. The subject methods and systems also find use in optical detection systems used to analyze and sort particle components in samples in fluid media, such as biological samples. The present disclosure also finds use in flow cytometry, where it is desirable to provide a flow cytometer with improved cell sorting accuracy, increased particle collection, reduced energy consumption, particle charging efficiency, more accurate particle charging, and improved particle deflection during cell sorting. In embodiments, the present disclosure reduces the need for user input or manual adjustments during sample analysis by a flow cytometer. In certain embodiments, the subject methods and systems provide fully automated protocols so that little, if any, human input is required to adjust the flow cytometer during use.

[0197] The following examples are offered by way of illustration and not by way of limitation. [Example]

[0198] I. Detector-Setting-Independent Re-Scaling of FACS Chorus Experimental Data A. Background Flow cytometry signals are scaled on an arbitrary unit axis. The maximum pulse height is, for example, 2 for FACS Discover. 16 The maximum pulse area is defined as follows:

[0199] Pulse Area max = bit depth x pulse length [μs] x sampling rate [MHz]

[0200] Pulse magnitude relates to the signal above noise at a given detector amplification. As a result, pulse height / area increases as detector amplification increases. This arbitrary unit scaling is dependent on detector settings, which introduces ambiguity into the interpretation of the underlying data.

[0201] In the next generation of standardization, the goal is to enable users to view their data in a Detector Setting Independent (DSI) manner. This is achieved by implementing DSI scaling factors that can be transposed between detector settings by taking advantage of the fundamental linearity of detector gain with respect to signal magnitude. This linearity is achieved by performing a detector setting calibration during baseline setup, for example, as shown in Figure 8.

[0202] The resulting detector calibration yields a linear relationship between pulse magnitude and gain, which can be used to normalize the data to a common scale, for example as shown in Figure 9. This allows for comparison of data not only between experiments but also between setups, with results proportional to the underlying signal before it reaches the detector.

[0203] B. Chorus experiment When a user opens a new Chorus experiment, the detector settings will default to the biological assay TTV settings. These assay settings are a set of detector settings that should work well across a wide range of assays, but may not necessarily be optimal for all assays. When opening an existing experiment, the detector settings will be set to the settings used when the experiment was last saved, but will be adjusted according to daily setup and QC to account for changes in the multispectral bead (MSB) target value within a given tolerance window. This gain adjustment is achieved using the following algorithm:

[0204]

number

[0205]

number

[0206] The gain found in the "Set Detector Gain" step is called the on-target gain. The mean fluorescence intensity (MFI) measured at the on-target gain is called the on-target MFI. They are recorded in the setup report. The ratio of the on-target MFI to the on-target gain in linear units, called the offset, is an important parameter for gain setup when a user opens a new or existing experiment.

[0207] The offset provides a detector setting independent MFI of the MSB when it is on-target and is calculated during daily setup and QC.

[0208]

number

[0209] This ABD scaling factor allows data to be scaled consistently across instruments and detector settings and across different MSB lots.

[0210] C. Implementation of detector setting independent scaling factors In typical flow cytometry applications, detector settings are used to ensure a sample, e.g., MSB, falls within a target window of any unit scale. If the sample MFI is too high in a particular channel, the detector setting is decreased; if it is too low, the detector setting is increased. The use of TTV facilitates this normalization across instruments over time to increase data consistency. By implementing a scaling factor that accounts for detector settings, it becomes feasible to optimize the detector independently of data scaling; data scaling instead becomes a digital correction factor.

[0211]

number

[0212] There are several approaches to implementing DSI scaling, each with its own implementation and workflow advantages.

[0213] 1. Scaling Factor a.ABD The use of ABD values ​​to rescale the data directly uses the current implementation of TTV. ABD values ​​are assigned to the MSB for each detector channel. This results in detector- and instrument-independent data, whose accuracy is limited to the ABD assignment accuracy of the MSB. This accuracy is the same as the accuracy of daily MFI normalization using detector gain adjustments.

[0214] DSI coefficient = TTV (current)

[0215] D. Changing the Spectral Separation 1. The current spectral unmixing workflow roughly follows this sequence: a. Obtain NSOV from single stain analysis (delta MFI, divided by TTV used during acquisition).

[0216]

number

[0217] b. Convert NSOV to sov' denormalized value by multiplying by the current TTV.

[0218] sov'=nsov Norm ×TTV(current)

[0219] c. Divide the sov' of each fluorophore by the maximum value to generate an SOV matrix that falls between 0 and 1.

[0220]

number

[0221] d. The resulting matrix is ​​used to separate the raw signal data into its individual components. Converting the separation data to DIS scaling can be done upstream or downstream of the separation process as follows: For ergonomic reasons, a downstream approach is preferred.

[0222] 2.ABD This is done by dividing the separated data for each fluorophore by finding the TTV of the maximum detector signal from sov' as follows: Let sov = [s1,s2,...,sn] and TTV = [t1,t2,...,tn]. Define the index of the maximum element in sov' as follows:

[0223]

number

[0224] In that case, the corresponding value of TTV is:

[0225]

number

[0226] 3. Default Isolation The SOV is calculated as follows:

[0227]

number

[0228] 4. Implementation of ABD The two formulas above are updated as follows:

[0229]

number

[0230] E. Changing the visualization of data For data scaled by the default method, it is typically possible to find a biexponential scaling criterion or sigma of inverse hyperbolic scaling that is applicable across the gain of a given detector. However, these criteria may vary between detectors (Figure 10, Panel A). With the introduction of ABD-scaled data, the biexponential or sigma criterion needs to be adjusted with the detector gain (Figure 10, Panel B). Furthermore, the gates drawn on the axes ideally need to be transposed between the default and DSI modes. One method for dynamic display scaling of axes and gating is identified and implemented. The bi-exponential scaling function is defined as follows:

[0231]

number

[0232] where: S: Raw linear signal strength (photons, ADC counts, ...) X∈[0,M]: biexponential axis values ​​shown on the plot T:X=Maximum raw value to be mapped to M M: Positive dynamic range in tens of units (unitless) W: Half-width in tens of units of the linear region around zero (unitless)

[0233]

number

[0234] p: parameter chosen so that the slope at X = W is exactly 1

[0235]

number

[0236] 1. Linear rescaling of raw data Once the raw data is rescaled by the DSI coefficients, the biexponential scaling coefficients of the raw data are adjusted to take into account the new scaling but still appear visually the same.

[0237] S=T10- (M-W) and 10- (M-W) Therefore, T ref can simply be rescaled by the DSI factor. In order to accurately recalculate the half-width W, we also need to rescale r by the DSI factor to keep the ratio of r to T the same, and therefore keep W fixed. The new scaling factors are:

[0238]

number

[0239] An example of this implementation is provided in FIG.

[0240] Notwithstanding the scope of the appended claims, the present disclosure is also defined by the following clauses. 1. A method comprising: detecting light from particles in a sample in the flowstream using a light detection system comprising a light detector; generating a data signal in response to the detected light; normalizing the data signal by the detector gain to generate a gain-normalized data signal; adjusting the gain-normalized data signal with a scaling factor to generate a scaled data signal; A method comprising: 2. The method of clause 1, wherein the scaling factor adjusts the gain-normalized data signal to a predetermined mean fluorescence intensity (MFI). 3. The method of clause 1 or 2, comprising calculating a scaling factor. 4. The scaling factor is determining a linear gain as a function of photodetector voltage; determining a gain corresponding to a predetermined mean fluorescence intensity; calculating a scaling factor that adjusts the generated gain-normalized data signal to a predetermined mean fluorescence intensity; Calculated by the method described in clause 3. 5. The method of clause 4, wherein the linear gain is derived from a look-up table. 6. Illuminating a photodetector with a plurality of light sources of different intensities; Detecting light from a light source of a plurality of different intensities at a plurality of different photodetector voltages; Determining the detector gain setting of the photodetector sufficient to produce a mean fluorescence intensity that increases linearly with detector gain; 6. The method of claim 4 or 5, comprising determining a linear gain as a function of photodetector voltage by: 7. The method of clause 6, wherein the light source comprises a light emitting diode (LED). 8. A given mean fluorescence intensity is illuminating a reference particle with a light source; detecting fluorescence from the reference particle; 8. The method according to any one of clauses 2 to 7, wherein the 9. The method of clause 8, wherein the reference particles comprise multispectral fluorescent beads. 10. The method of any one of clauses 1 to 9, wherein the detector gain used to normalize the data signal is the gain of the photodetector at a given mean fluorescence intensity. 11. The method of any one of clauses 1-10, further comprising adjusting the gain-normalized data signal with a calibration factor. 12. The method of clause 11, wherein the calibration factor adjusts the gain-normalized data signal in response to changes in particle velocity in the flow stream. 13. The method of clause 11, wherein the calibration factor adjusts the gain-normalized data signal in response to changes in the laser intensity of the light source. 14. Spectrally separating the gain-normalized data signal; adjusting the spectrally separated data signal with a scaling factor to generate a scaled separated data signal; 14. The method of any one of clauses 1 to 13, comprising: 15. The method of any one of clauses 1 to 14, wherein light is detected in multiple photodetector channels. 16. The method of any one of clauses 1-15, further comprising illuminating the sample with a light source. 17. The method of clause 16, wherein the light source comprises a laser. 18. The method of clause 17, wherein the light source comprises a plurality of lasers. 19. The method of any one of clauses 1 to 18, wherein the optical detection system comprises a plurality of optical detectors. 20. The method of clause 19, wherein the photodetector comprises one or more photomultiplier tubes. 21. The method of any one of clauses 1 to 20, wherein the optical detection system comprises an optical detector array. 22. The method of clause 21, wherein the photodetector array comprises photodiodes. 23. The method of clause 22, wherein the photodetector array comprises a charge-coupled device. 24. A light source configured to illuminate a sample containing particles in a flow stream; a light detection system including a light detector for detecting light from the illuminated particles; Processor and a processor comprising a memory operatively coupled to the processor, the memory storing instructions, the instructions, when executed by the processor, causing the processor to: generating a data signal in response to the detected light; normalizing the data signal by the detector gain to generate a gain-normalized data signal; A system for adjusting a gain-normalized data signal with a scaling factor to generate a scaled data signal. 25. The system of clause 24, configured to detect light in a plurality of photodetector channels by a light detection system. 26. The system of clause 24 or 25, wherein the memory includes instructions for applying a scaling factor to adjust the gain-normalized data signal to a predetermined mean fluorescence intensity (MFI). 27. A system according to any one of clauses 24 to 26, wherein the memory includes instructions for calculating a scaling factor. 28. Memory, determining a linear gain as a function of photodetector voltage; determining a gain corresponding to a predetermined mean fluorescence intensity; calculating a scaling factor that adjusts the generated gain-normalized data signal to a predetermined mean fluorescence intensity; 28. The system of claim 27, including instructions for calculating the scaling factor by: 29. The system of clause 28, wherein the memory includes instructions for deriving the linear gain from a lookup table. 30. Memory, illuminating a photodetector with a plurality of light sources of different intensities; Detecting light from a light source of a plurality of different intensities at a plurality of different photodetector voltages; Determining the detector gain setting of the photodetector sufficient to produce a mean fluorescence intensity that increases linearly with detector gain; 30. The system of claim 28 or 29, including instructions for determining linear gain as a function of photodetector voltage by: 31. The system of clause 30, wherein the light source comprises a light emitting diode. 32. Memory, illuminating a reference particle with a light source; detecting fluorescence from the reference particle; 32. The system of any one of clauses 25 to 31, comprising instructions for determining a predetermined mean fluorescence intensity by: 33. The system of clause 32, wherein the reference particles include multispectral beads. 34. A system described in any one of clauses 24 to 33, wherein the detector gain used to normalize the data signal is the gain of the photodetector at a given mean fluorescence intensity. 35. A system as described in any one of clauses 24 to 34, wherein the memory includes instructions for adjusting the gain-normalized data signal with a calibration factor. 36. The system of clause 35, wherein the calibration factor adjusts the gain-normalized data signal in response to changes in particle velocity in the flow stream. 37. The system of clause 35, wherein the calibration factor adjusts the gain-normalized data signal in response to changes in the laser intensity of the light source. 38. Memory, spectrally separating the gain-normalized data signal; adjusting the spectrally separated data signal with a scaling factor to generate a scaled separated data signal; 38. A system as described in any one of clauses 24 to 37, including instructions for: 39. A system according to any one of clauses 24 to 38, wherein the light source comprises a laser. 40. The system of clause 39, wherein the light source comprises a plurality of lasers. 41. A system according to any one of clauses 24 to 40, wherein the optical detection system comprises a plurality of optical detectors. 42. The system of clause 41, wherein the photodetector comprises one or more photomultiplier tubes. 43. A system according to any one of clauses 24 to 40, wherein the optical detection system comprises an optical detector array. 44. The system of clause 43, wherein the photodetector array comprises photodiodes. 45. The system of clause 44, wherein the photodetector array comprises a charge-coupled device. 46. ​​A non-transitory computer-readable storage medium having instructions stored thereon, comprising: an algorithm for detecting light from particles in a sample in a flow stream using a light detection system comprising a light detector; an algorithm for generating a data signal in response to the detected light; an algorithm for normalizing the data signal by the detector gain to produce a gain-normalized data signal; an algorithm for adjusting the gain-normalized data signal with a scaling factor to generate a scaled data signal; 1. A non-transitory computer-readable storage medium comprising: 47. The non-transitory computer-readable storage medium of clause 46, comprising an algorithm for applying a scaling factor that adjusts the gain-normalized data signal to a predetermined mean fluorescence intensity (MFI). 48. A non-transitory computer-readable storage medium according to clause 46 or 47, comprising an algorithm for calculating scaling factors. 49. An algorithm for determining linear gain as a function of photodetector voltage; an algorithm for determining a gain corresponding to a given mean fluorescence intensity; an algorithm for calculating a scaling factor that adjusts the generated gain-normalized data signal to a given mean fluorescence intensity; 49. A non-transitory computer-readable storage medium as set forth in clause 48, comprising: 50. A non-transitory computer-readable storage medium as set forth in clause 49, comprising an algorithm for deriving a linear gain from a look-up table. 51. An algorithm for illuminating a photodetector with a plurality of light sources of different intensities; an algorithm for detecting light from a plurality of different intensities of the light source at a plurality of different photodetector voltages; An algorithm to determine the detector gain setting of a photodetector sufficient to produce a mean fluorescence intensity that increases linearly with detector gain. 51. A non-transitory computer-readable storage medium according to clause 49 or 50, comprising: 52. The non-transitory computer-readable storage medium of clause 51, wherein the light source comprises a light emitting diode (LED). 53. An algorithm for illuminating a reference particle with a light source; Algorithm for detecting fluorescence from reference particles and 53. The non-transitory computer readable storage medium of any one of clauses 47 to 52, comprising an algorithm for determining a predetermined mean fluorescence intensity, comprising: 54. The non-transitory computer-readable storage medium of clause 53, wherein the reference particles include multispectral fluorescent beads. 55. A non-transitory computer-readable storage medium according to any one of clauses 46 to 54, comprising an algorithm for normalizing the data signal using a detector gain, the detector gain being the gain of the photodetector at a given mean fluorescence intensity. 56. A non-transitory computer-readable storage medium according to any one of clauses 46 to 55, comprising an algorithm for adjusting a gain-normalized data signal with a calibration factor. 57. The non-transitory computer-readable storage medium of clause 56, wherein the calibration coefficient adjusts the gain-normalized data signal in response to changes in particle velocity in the flow stream. 58. The non-transitory computer-readable storage medium of clause 56, wherein the calibration coefficient adjusts the gain-normalized data signal in response to changes in laser intensity of the light source. 59. An algorithm for spectrally separating a gain-normalized data signal; an algorithm for adjusting the spectrally separated data signal with a scaling factor to generate a scaled separated data signal; 59. A non-transitory computer-readable storage medium according to any one of clauses 46 to 58, comprising:

[0241] Although the foregoing disclosure has been described in some detail by way of illustration and example for clarity of understanding, it will be readily apparent to those skilled in the art in light of the teachings of the present disclosure that certain changes and modifications can be made without departing from the spirit or scope of the appended claims.

[0242] Thus, the foregoing merely illustrates the principles of the present disclosure. It will be appreciated that those skilled in the art will be able to devise various configurations, not explicitly described or shown herein, that embody the principles of the present disclosure and are within its spirit and scope. Furthermore, all examples and conditional language recited herein are intended primarily to aid the reader in understanding the principles of the present disclosure and the concepts that the present disclosure has contributed to advancing the art, and should not be construed as being limited to such specifically recited examples and conditions. Furthermore, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Furthermore, such equivalents are intended to include both currently known equivalents and equivalents developed in the future, i.e., any elements developed that perform the same function, regardless of structure. Furthermore, nothing disclosed herein is intended to be made available to the public, regardless of whether such disclosure is expressly recited in the claims.

[0243] Accordingly, the scope of the present disclosure is not intended to be limited to the exemplary embodiments shown and described herein. Rather, the scope and spirit of the present disclosure are embodied by the appended claims. In the claims, 35 U.S.C. §112(f) or 35 U.S.C. §112(6) is expressly defined as being invoked for a limitation in a claim only if the exact phrase "means for" or the exact phrase "step" appears at the beginning of such limitation in the claim; if such exact phrase is not used in a limitation in a claim, 35 U.S.C. §112(f) or 35 U.S.C. §112(6) is not invoked.

[0244] CROSS-REFERENCE TO RELATED APPLICATIONS Pursuant to 35 U.S.C. § 119(e), this application claims priority to the filing date of U.S. Provisional Patent Application No. 63 / 663,613, filed June 24, 2024, the disclosure of which is incorporated herein by reference in its entirety.

Claims

1. detecting light from particles in a sample in the flowstream using a light detection system comprising a light detector; generating a data signal in response to the detected light; normalizing the data signal by a detector gain to generate a gain-normalized data signal; adjusting the gain-normalized data signal with a scaling factor to generate a scaled data signal; A method comprising:

2. The method of claim 1 , wherein the scaling factor adjusts the gain-normalized data signal to a predetermined mean fluorescence intensity (MFI).

3. The method of claim 1 or 2, comprising calculating the scaling factor.

4. The scaling factor is determining a linear gain as a function of photodetector voltage; determining a gain corresponding to the predetermined mean fluorescence intensity; calculating the scaling factor that scales the generated gain-normalized data signal to the predetermined mean fluorescence intensity; The method of claim 3, wherein the calculated value is:

5. The method of claim 4 , wherein the linear gain is derived from a look-up table.

6. illuminating the photodetector with a plurality of light sources of different intensities; detecting light from the light source at the plurality of different intensities at a plurality of different photodetector voltages; determining a detector gain setting for the photodetector sufficient to produce a mean fluorescence intensity that increases linearly with the detector gain; 6. The method of claim 4, comprising determining the linear gain as a function of photodetector voltage by:

7. The predetermined mean fluorescence intensity is illuminating a reference particle with a light source; detecting fluorescence from the reference particles; The method according to any one of claims 2 to 6, wherein the value is determined by

8. 8. The method of claim 1, wherein the detector gain used to normalize the data signal is the gain of the photodetector at the given mean fluorescence intensity.

9. The method of any one of claims 1 to 8, further comprising adjusting the gain-normalized data signal with a calibration factor.

10. spectrally separating the gain-normalized data signal; adjusting the spectrally separated data signal with the scaling factor to generate a scaled separated data signal; The method according to any one of claims 1 to 9, comprising:

11. The method of any one of claims 1 to 10, wherein light is detected in multiple photodetector channels.

12. The method of any one of claims 1 to 11, further comprising illuminating the sample with a light source.

13. 20. The method of claim 19, wherein the photodetector comprises one or more photomultiplier tubes.

13. The method of any one of claims 1 to 12, wherein the light detection system comprises a light detector array.

14. a light source configured to illuminate a sample containing particles in the flow stream; a light detection system comprising a light detector for detecting light from the illuminated particles; Processor and the processor comprising a memory operatively coupled to the processor, the memory storing instructions that, when executed by the processor, cause the processor to: generating a data signal in response to the detected light; normalizing the data signal by a detector gain to generate a gain-normalized data signal; The system adjusts the gain-normalized data signal with a scaling factor to generate a scaled data signal.

15. A non-transitory computer-readable storage medium having instructions stored thereon, comprising: an algorithm for detecting light from particles in a sample in a flow stream using a light detection system comprising a light detector; an algorithm for generating a data signal in response to the detected light; an algorithm for normalizing the data signal by a detector gain to produce a gain-normalized data signal; an algorithm for adjusting the gain-normalized data signal with a scaling factor to generate a scaled data signal; an algorithm for calculating the scaling factor that adjusts the generated gain-normalized data signal to the predetermined mean fluorescence intensity; 1. A non-transitory computer-readable storage medium comprising:

Images